An optimal control strategy for hybrid actuator systems: Application to an artificial muscle with electric motor assist.

Science.gov (United States)

Ishihara, Koji; Morimoto, Jun

2018-03-01

Humans use multiple muscles to generate such joint movements as an elbow motion. With multiple lightweight and compliant actuators, joint movements can also be efficiently generated. Similarly, robots can use multiple actuators to efficiently generate a one degree of freedom movement. For this movement, the desired joint torque must be properly distributed to each actuator. One approach to cope with this torque distribution problem is an optimal control method. However, solving the optimal control problem at each control time step has not been deemed a practical approach due to its large computational burden. In this paper, we propose a computationally efficient method to derive an optimal control strategy for a hybrid actuation system composed of multiple actuators, where each actuator has different dynamical properties. We investigated a singularly perturbed system of the hybrid actuator model that subdivided the original large-scale control problem into smaller subproblems so that the optimal control outputs for each actuator can be derived at each control time step and applied our proposed method to our pneumatic-electric hybrid actuator system. Our method derived a torque distribution strategy for the hybrid actuator by dealing with the difficulty of solving real-time optimal control problems. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

A Hybrid Optimization Method for Reactive Power and Voltage Control Considering Power Loss Minimization

DEFF Research Database (Denmark)

Liu, Chengxi; Qin, Nan; Bak, Claus Leth

2015-01-01

This paper proposes a hybrid optimization method to optimally control the voltage and reactive power with minimum power loss in transmission grid. This approach is used for the Danish automatic voltage control (AVC) system which is typically a non-linear non-convex problem mixed with both...

Super-capacitors fuel-cell hybrid electric vehicle optimization and control strategy development

International Nuclear Information System (INIS)

Paladini, Vanessa; Donateo, Teresa; De Risi, Arturo; Laforgia, Domenico

2007-01-01

In the last decades, due to emissions reduction policies, research focused on alternative powertrains among which hybrid electric vehicles (HEVs) powered by fuel cells are becoming an attractive solution. One of the main issues of these vehicles is the energy management in order to improve the overall fuel economy. The present investigation aims at identifying the best hybrid vehicle configuration and control strategy to reduce fuel consumption. The study focuses on a car powered by a fuel cell and equipped with two secondary energy storage devices: batteries and super-capacitors. To model the powertrain behavior an on purpose simulation program called ECoS has been developed in Matlab/Simulink environment. The fuel cell model is based on the Amphlett theory. The battery and the super-capacitor models account for charge/discharge efficiency. The analyzed powertrain is also equipped with an energy regeneration system to recover braking energy. The numerical optimization of vehicle configuration and control strategy of the hybrid electric vehicle has been carried out with a multi objective genetic algorithm. The goal of the optimization is the reduction of hydrogen consumption while sustaining the battery state of charge. By applying the algorithm to different driving cycles, several optimized configurations have been identified and discussed

Switching and optimizing control for coal flotation process based on a hybrid model

Science.gov (United States)

Dong, Zhiyong; Wang, Ranfeng; Fan, Minqiang; Fu, Xiang

2017-01-01

Flotation is an important part of coal preparation, and the flotation column is widely applied as efficient flotation equipment. This process is complex and affected by many factors, with the froth depth and reagent dosage being two of the most important and frequently manipulated variables. This paper proposes a new method of switching and optimizing control for the coal flotation process. A hybrid model is built and evaluated using industrial data. First, wavelet analysis and principal component analysis (PCA) are applied for signal pre-processing. Second, a control model for optimizing the set point of the froth depth is constructed based on fuzzy control, and a control model is designed to optimize the reagent dosages based on expert system. Finally, the least squares-support vector machine (LS-SVM) is used to identify the operating conditions of the flotation process and to select one of the two models (froth depth or reagent dosage) for subsequent operation according to the condition parameters. The hybrid model is developed and evaluated on an industrial coal flotation column and exhibits satisfactory performance. PMID:29040305

Modeling and Optimal Control of a Class of Warfare Hybrid Dynamic Systems Based on Lanchester (n,1 Attrition Model

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Xiangyong Chen

2014-01-01

hybrid dynamic systems is established based on Lanchester equation in a (n,1 battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation, and discrete event systems (described by variable tactics. Furthermore, an expository discussion is presented on an optimal variable tactics control problem for warfare hybrid dynamic system. The optimal control strategies are designed based on dynamic programming and differential game theory. As an example of the consequences of this optimal control problem, we take the (2, 1 case and solve the optimal strategies in a (2, 1 case. Simulation results show the feasibility of warfare hybrid system model and the effectiveness of the optimal control strategies designed.

Optimal control of stretching process of flexible solar arrays on spacecraft based on a hybrid optimization strategy

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Qijia Yao

2017-07-01

Full Text Available The optimal control of multibody spacecraft during the stretching process of solar arrays is investigated, and a hybrid optimization strategy based on Gauss pseudospectral method (GPM and direct shooting method (DSM is presented. First, the elastic deformation of flexible solar arrays was described approximately by the assumed mode method, and a dynamic model was established by the second Lagrangian equation. Then, the nonholonomic motion planning problem is transformed into a nonlinear programming problem by using GPM. By giving fewer LG points, initial values of the state variables and control variables were obtained. A serial optimization framework was adopted to obtain the approximate optimal solution from a feasible solution. Finally, the control variables were discretized at LG points, and the precise optimal control inputs were obtained by DSM. The optimal trajectory of the system can be obtained through numerical integration. Through numerical simulation, the stretching process of solar arrays is stable with no detours, and the control inputs match the various constraints of actual conditions. The results indicate that the method is effective with good robustness. Keywords: Motion planning, Multibody spacecraft, Optimal control, Gauss pseudospectral method, Direct shooting method

Hybrid intelligent control concepts for optimal data fusion

Science.gov (United States)

Llinas, James

1994-02-01

In the post-Cold War era, Naval surface ship operations will be largely conducted in littoral waters to support regional military missions of all types, including humanitarian and evacuation activities, and amphibious mission execution. Under these conditions, surface ships will be much more isolated and vulnerable to a variety of threats, including maneuvering antiship missiles. To deal with these threats, the optimal employment of multiple shipborne sensors for maximum vigilance is paramount. This paper characterizes the sensor management problem as one of intelligent control, identifies some of the key issues in controller design, and presents one approach to controller design which is soon to be implemented and evaluated. It is argued that the complexity and hierarchical nature of problem formulation demands a hybrid combination of knowledge-based methods and scheduling techniques from 'hard' real-time systems theory for its solution.

Optimal management with hybrid dynamics : The shallow lake problem

NARCIS (Netherlands)

Reddy, P.V.; Schumacher, Hans; Engwerda, Jacob; Camlibel, M.K.; Julius, A.A.; Pasumarthy, R.

2015-01-01

In this article we analyze an optimal management problem that arises in ecological economics using hybrid systems modeling. First, we introduce a discounted autonomous infinite horizon hybrid optimal control problem and develop few tools to analyze the necessary conditions for optimality. Next,

Conceptual Design and Optimal Power Control Strategy for AN Eco-Friendly Hybrid Vehicle

Science.gov (United States)

Nasiri, N. Mir; Chieng, Frederick T. A.

2011-06-01

This paper presents a new concept for a hybrid vehicle using a torque and speed splitting technique. It is implemented by the newly developed controller in combination with a two degree of freedom epicyclic gear transmission. This approach enables optimization of the power split between the less powerful electrical motor and more powerful engine while driving a car load. The power split is fundamentally a dual-energy integration mechanism as it is implemented by using the epicyclic gear transmission that has two inputs and one output for a proper power distribution. The developed power split control system manages the operation of both the inputs to have a known output with the condition of maintaining optimum operating efficiency of the internal combustion engine and electrical motor. This system has a huge potential as it is possible to integrate all the features of hybrid vehicle known to-date such as the regenerative braking system, series hybrid, parallel hybrid, series/parallel hybrid, and even complex hybrid (bidirectional). By using the new power split system it is possible to further reduce fuel consumption and increase overall efficiency.

An optimal control-based algorithm for hybrid electric vehicle using preview route information

NARCIS (Netherlands)

Ngo, D.V.; Hofman, T.; Steinbuch, M.; Serrarens, A.F.A.

2010-01-01

Control strategies for Hybrid Electric Vehicles (HEVs) are generally aimed at optimally choosing the power distribution between the internal combustion engine and the electric motor in order to minimize the fuel consumption and/or emissions. Using vehicle navigation systems in combination with

Optimal Design and Hybrid Control for the Electro-Hydraulic Dual-Shaking Table System

Directory of Open Access Journals (Sweden)

Lianpeng Zhang

2016-08-01

Full Text Available This paper is to develop an optimal electro-hydraulic dual-shaking table system with high waveform replication precision. The parameters of hydraulic cylinders, servo valves, hydraulic supply power and gravity balance system are designed and optimized in detail. To improve synchronization and tracking control precision, a hybrid control strategy is proposed. The cross-coupled control using a novel based on sliding mode control based on adaptive reaching law (ASMC, which can adaptively tune the parameters of sliding mode control (SMC, is proposed to reduce the synchronization error. To improve the tracking performance, the observer-based inverse control scheme combining the feed-forward inverse model controller and disturbance observer is proposed. The system model is identified applying the recursive least squares (RLS algorithm and then the feed-forward inverse controller is designed based on zero phase error tracking controller (ZPETC technique. To compensate disturbance and model errors, disturbance observer is used cooperating with the designed inverse controller. The combination of the novel ASMC cross-coupled controller and proposed observer-based inverse controller can improve the control precision noticeably. The dual-shaking table experiment system is built and various experiments are performed. The experimental results indicate that the developed system with the proposed hybrid control strategy is feasible and efficient and can reduce the tracking errors to 25% and synchronization error to 16% compared with traditional control schemes.

Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle

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Linhui Li

2014-01-01

Full Text Available Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

IMPULSE CONTROL HYBRID ELECTRICAL SYSTEM

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A. A. Lobaty

2016-01-01

Full Text Available This paper extends the recently introduced approach for modeling and solving the optimal control problem of fixedswitched mode DC-DC power converter. DCDC converters are a class of electric power circuits that used extensively in regulated DC power supplies, DC motor drives of different types, in Photovoltaic Station energy conversion and other applications due to its advantageous features in terms of size, weight and reliable performance. The main problem in controlling this type converters is in their hybrid nature as the switched circuit topology entails different modes of operation, each of it with its own associated linear continuous-time dynamics.This paper analyses the modeling and controller synthesis of the fixed-frequency buck DC-DC converter, in which the transistor switch is operated by a pulse sequence with constant frequency. In this case the regulation of the DC component of the output voltage is via the duty cycle. The optimization of the control system is based on the formation of the control signal at the output.It is proposed to solve the problem of optimal control of a hybrid system based on the formation of the control signal at the output of the controller, which minimizes a given functional integral quality, which is regarded as a linear quadratic Letov-Kalman functional. Search method of optimal control depends on the type of mathematical model of control object. In this case, we consider a linear deterministic model of the control system, which is common for the majority of hybrid electrical systems. For this formulation of the optimal control problem of search is a problem of analytical design of optimal controller, which has the analytical solution.As an example of the hybrid system is considered a step-down switching DC-DC converter, which is widely used in various electrical systems: as an uninterruptible power supply, battery charger for electric vehicles, the inverter in solar photovoltaic power plants.. A

A Frequency Control Approach for Hybrid Power System Using Multi-Objective Optimization

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Mohammed Elsayed Lotfy

2017-01-01

Full Text Available A hybrid power system uses many wind turbine generators (WTG and solar photovoltaics (PV in isolated small areas. However, the output power of these renewable sources is not constant and can diverge quickly, which has a serious effect on system frequency and the continuity of demand supply. In order to solve this problem, this paper presents a new frequency control scheme for a hybrid power system to ensure supplying a high-quality power in isolated areas. The proposed power system consists of a WTG, PV, aqua-electrolyzer (AE, fuel cell (FC, battery energy storage system (BESS, flywheel (FW and diesel engine generator (DEG. Furthermore, plug-in hybrid electric vehicles (EVs are implemented at the customer side. A full-order observer is utilized to estimate the supply error. Then, the estimated supply error is considered in a frequency domain. The high-frequency component is reduced by BESS and FW; while the low-frequency component of supply error is mitigated using FC, EV and DEG. Two PI controllers are implemented in the proposed system to control the system frequency and reduce the supply error. The epsilon multi-objective genetic algorithm ( ε -MOGA is applied to optimize the controllers’ parameters. The performance of the proposed control scheme is compared with that of recent well-established techniques, such as a PID controller tuned by the quasi-oppositional harmony search algorithm (QOHSA. The effectiveness and robustness of the hybrid power system are investigated under various operating conditions.

Optimal control on hybrid ode systems with application to a tick disease model.

Science.gov (United States)

Ding, Wandi

2007-10-01

We are considering an optimal control problem for a type of hybrid system involving ordinary differential equations and a discrete time feature. One state variable has dynamics in only one season of the year and has a jump condition to obtain the initial condition for that corresponding season in the next year. The other state variable has continuous dynamics. Given a general objective functional, existence, necessary conditions and uniqueness for an optimal control are established. We apply our approach to a tick-transmitted disease model with age structure in which the tick dynamics changes seasonally while hosts have continuous dynamics. The goal is to maximize disease-free ticks and minimize infected ticks through an optimal control strategy of treatment with acaricide. Numerical examples are given to illustrate the results.

Optimal planning approaches with multiple impulses for rendezvous based on hybrid genetic algorithm and control method

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JingRui Zhang

2015-03-01

Full Text Available In this article, we focus on safe and effective completion of a rendezvous and docking task by looking at planning approaches and control with fuel-optimal rendezvous for a target spacecraft running on a near-circular reference orbit. A variety of existent practical path constraints are considered, including the constraints of field of view, impulses, and passive safety. A rendezvous approach is calculated by using a hybrid genetic algorithm with those constraints. Furthermore, a control method of trajectory tracking is adopted to overcome the external disturbances. Based on Clohessy–Wiltshire equations, we first construct the mathematical model of optimal planning approaches of multiple impulses with path constraints. Second, we introduce the principle of hybrid genetic algorithm with both stronger global searching ability and local searching ability. We additionally explain the application of this algorithm in the problem of trajectory planning. Then, we give three-impulse simulation examples to acquire an optimal rendezvous trajectory with the path constraints presented in this article. The effectiveness and applicability of the tracking control method are verified with the optimal trajectory above as control objective through the numerical simulation.

Design of Optimal Hybrid Position/Force Controller for a Robot Manipulator Using Neural Networks

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Vikas Panwar

2007-01-01

Full Text Available The application of quadratic optimization and sliding-mode approach is considered for hybrid position and force control of a robot manipulator. The dynamic model of the manipulator is transformed into a state-space model to contain two sets of state variables, where one describes the constrained motion and the other describes the unconstrained motion. The optimal feedback control law is derived solving matrix differential Riccati equation, which is obtained using Hamilton Jacobi Bellman optimization. The optimal feedback control law is shown to be globally exponentially stable using Lyapunov function approach. The dynamic model uncertainties are compensated with a feedforward neural network. The neural network requires no preliminary offline training and is trained with online weight tuning algorithms that guarantee small errors and bounded control signals. The application of the derived control law is demonstrated through simulation with a 4-DOF robot manipulator to track an elliptical planar constrained surface while applying the desired force on the surface.

Optimal energy management of HEVs with hybrid storage system

International Nuclear Information System (INIS)

Vinot, E.; Trigui, R.

2013-01-01

Highlights: • A battery and ultra-capacitor system for parallel hybrid vehicle is considered. • Optimal management using Pontryagin’s minimum principle is developed. • Battery stress limitation is taken into account by means of RMS current. • Rule based management approaching the optimal control is proposed. • Comparison between rule based and optimal management are proposed using Pareto front. - Abstract: Energy storage systems are a key point in the design and development of electric and hybrid vehicles. In order to reduce the battery size and its current stress, a hybrid storage system, where a battery is coupled with an electrical double-layer capacitor (EDLC) is considered in this paper. The energy management of such a configuration is not obvious and the optimal operation concerning the energy consumption and battery RMS current has to be identified. Most of the past work on the optimal energy management of HEVs only considered one additional power source. In this paper, the control of a hybrid vehicle with a hybrid storage system (HSS), where two additional power sources are used, is presented. Applying the Pontryagin’s minimum principle, an optimal energy management strategy is found and compared to a rule-based parameterized control strategy. Simulation results are shown and discussed. Applied on a small compact car, optimal and ruled-based methods show that gains of fuel consumption and/or a battery RMS current higher than 15% may be obtained. The paper also proves that a well tuned rule-based algorithm presents rather good performances when compared to the optimal strategy and remains relevant for different driving cycles. This rule-based algorithm may easily be implemented in a vehicle prototype or in an HIL test bench

Stillwater Hybrid Geo-Solar Power Plant Optimization Analyses

Energy Technology Data Exchange (ETDEWEB)

Wendt, Daniel S.; Mines, Gregory L.; Turchi, Craig S.; Zhu, Guangdong; Cohan, Sander; Angelini, Lorenzo; Bizzarri, Fabrizio; Consoli, Daniele; De Marzo, Alessio

2015-09-02

The Stillwater Power Plant is the first hybrid plant in the world able to bring together a medium-enthalpy geothermal unit with solar thermal and solar photovoltaic systems. Solar field and power plant models have been developed to predict the performance of the Stillwater geothermal / solar-thermal hybrid power plant. The models have been validated using operational data from the Stillwater plant. A preliminary effort to optimize performance of the Stillwater hybrid plant using optical characterization of the solar field has been completed. The Stillwater solar field optical characterization involved measurement of mirror reflectance, mirror slope error, and receiver position error. The measurements indicate that the solar field may generate 9% less energy than the design value if an appropriate tracking offset is not employed. A perfect tracking offset algorithm may be able to boost the solar field performance by about 15%. The validated Stillwater hybrid plant models were used to evaluate hybrid plant operating strategies including turbine IGV position optimization, ACC fan speed and turbine IGV position optimization, turbine inlet entropy control using optimization of multiple process variables, and mixed working fluid substitution. The hybrid plant models predict that each of these operating strategies could increase net power generation relative to the baseline Stillwater hybrid plant operations.

Dynamic Modeling and Control Strategy Optimization for a Hybrid Electric Tracked Vehicle

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Hong Wang

2015-01-01

Full Text Available A new hybrid electric tracked bulldozer composed of an engine generator, two driving motors, and an ultracapacitor is put forward, which can provide high efficiencies and less fuel consumption comparing with traditional ones. This paper first presents the terramechanics of this hybrid electric tracked bulldozer. The driving dynamics for this tracked bulldozer is then analyzed. After that, based on analyzing the working characteristics of the engine, generator, and driving motors, the power train system model and control strategy optimization is established by using MATLAB/Simulink and OPTIMUS software. Simulation is performed under a representative working condition, and the results demonstrate that fuel economy of the HETV can be significantly improved.

Hierarchical models and iterative optimization of hybrid systems

Energy Technology Data Exchange (ETDEWEB)

Rasina, Irina V. [Ailamazyan Program Systems Institute, Russian Academy of Sciences, Peter One str. 4a, Pereslavl-Zalessky, 152021 (Russian Federation); Baturina, Olga V. [Trapeznikov Control Sciences Institute, Russian Academy of Sciences, Profsoyuznaya str. 65, 117997, Moscow (Russian Federation); Nasatueva, Soelma N. [Buryat State University, Smolina str.24a, Ulan-Ude, 670000 (Russian Federation)

2016-06-08

A class of hybrid control systems on the base of two-level discrete-continuous model is considered. The concept of this model was proposed and developed in preceding works as a concretization of the general multi-step system with related optimality conditions. A new iterative optimization procedure for such systems is developed on the base of localization of the global optimality conditions via contraction the control set.

Design Optimization of Hybrid FRP/RC Bridge

Science.gov (United States)

Papapetrou, Vasileios S.; Tamijani, Ali Y.; Brown, Jeff; Kim, Daewon

2018-04-01

The hybrid bridge consists of a Reinforced Concrete (RC) slab supported by U-shaped Fiber Reinforced Polymer (FRP) girders. Previous studies on similar hybrid bridges constructed in the United States and Europe seem to substantiate these hybrid designs for lightweight, high strength, and durable highway bridge construction. In the current study, computational and optimization analyses were carried out to investigate six composite material systems consisting of E-glass and carbon fibers. Optimization constraints are determined by stress, deflection and manufacturing requirements. Finite Element Analysis (FEA) and optimization software were utilized, and a framework was developed to run the complete analyses in an automated fashion. Prior to that, FEA validation of previous studies on similar U-shaped FRP girders that were constructed in Poland and Texas is presented. A finer optimization analysis is performed for the case of the Texas hybrid bridge. The optimization outcome of the hybrid FRP/RC bridge shows the appropriate composite material selection and cross-section geometry that satisfies all the applicable Limit States (LS) and, at the same time, results in the lightest design. Critical limit states show that shear stress criteria determine the optimum design for bridge spans less than 15.24 m and deflection criteria controls for longer spans. Increased side wall thickness can reduce maximum observed shear stresses, but leads to a high weight penalty. A taller cross-section and a thicker girder base can efficiently lower the observed deflections and normal stresses. Finally, substantial weight savings can be achieved by the optimization framework if base and side-wall thickness are treated as independent variables.

Engine Yaw Augmentation for Hybrid-Wing-Body Aircraft via Optimal Control Allocation Techniques

Science.gov (United States)

Taylor, Brian R.; Yoo, Seung Yeun

2011-01-01

Asymmetric engine thrust was implemented in a hybrid-wing-body non-linear simulation to reduce the amount of aerodynamic surface deflection required for yaw stability and control. Hybrid-wing-body aircraft are especially susceptible to yaw surface deflection due to their decreased bare airframe yaw stability resulting from the lack of a large vertical tail aft of the center of gravity. Reduced surface deflection, especially for trim during cruise flight, could reduce the fuel consumption of future aircraft. Designed as an add-on, optimal control allocation techniques were used to create a control law that tracks total thrust and yaw moment commands with an emphasis on not degrading the baseline system. Implementation of engine yaw augmentation is shown and feasibility is demonstrated in simulation with a potential drag reduction of 2 to 4 percent. Future flight tests are planned to demonstrate feasibility in a flight environment.

Speed control with torque ripple reduction of switched reluctance motor by Hybrid Many Optimizing Liaison Gravitational Search technique

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Nutan Saha

2017-06-01

Full Text Available This paper presents a control scheme for simultaneous control of the speed of Switched Reluctance Motor (SRM and minimizing the torque ripple employing Hybrid Many Optimizing Liaison Gravitational Search Algorithm (Hybrid MOLGSA technique. The control mechanism includes two controlling loops, the outer loop is governed for speed control and a current controller for the inner loop, intelligent selection of turn on and turn off angle for a 60 KW, 3-phase 6/8 SRM. It is noticed that the torque ripple coefficient, ISE of speed & current are reduced by 12.81%, 38.60%, 16.74% respectively by Hybrid MOLGSA algorithm compared to Gravitational Search Algorithm (GSA algorithm. It is also observed that the settling times for the controller using the parameter values for obtaining best values of torque ripple, Integral square error of speed and current are reduced by 51.25%, 58.04% and 59.375% by proposed Hybrid MOLGSA algorithm compared to the GSA algorithm.

An optimized Fuzzy Logic Controller by Water Cycle Algorithm for power management of Stand-alone Hybrid Green Power generation

International Nuclear Information System (INIS)

Sarvi, Mohammad; Avanaki, Isa Nasiri

2015-01-01

Highlights: • A new method to improve the performance of renewable power management is proposed. • The proposed method is based on Fuzzy Logic optimized by the Water Cycle Algorithm. • The proposed method characteristics are compared with two other methods. • The comparisons confirm that the proposed method is robust and effectiveness one. - Abstract: This paper aims to improve the power management system of a Stand-alone Hybrid Green Power generation based on the Fuzzy Logic Controller optimized by the Water Cycle Algorithm. The proposed Stand-alone Hybrid Green Power consists of wind energy conversion and photovoltaic systems as primary power sources and a battery, fuel cell, and Electrolyzer as energy storage systems. Hydrogen is produced from surplus power generated by the wind energy conversion and photovoltaic systems of Stand-alone Hybrid Green Power and stored in the hydrogen storage tank for fuel cell later using when the power generated by primary sources is lower than load demand. The proposed optimized Fuzzy Logic Controller based power management system determines the power that is generated by fuel cell or use by Electrolyzer. In a hybrid system, operation and maintenance cost and reliability of the system are the important issues that should be considered in studies. In this regard, Water Cycle Algorithm is used to optimize membership functions in order to simultaneously minimize the Loss of Power Supply Probability and operation and maintenance. The results are compared with the particle swarm optimization and the un-optimized Fuzzy Logic Controller power management system to prove that the proposed method is robust and effective. Reduction in Loss of Power Supply Probability and operation and maintenance, are the most advantages of the proposed method. Moreover the level of the State of Charge of the battery in the proposed method is higher than other mentioned methods which leads to increase battery lifetime.

Multiphase Return Trajectory Optimization Based on Hybrid Algorithm

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Yi Yang

2016-01-01

Full Text Available A hybrid trajectory optimization method consisting of Gauss pseudospectral method (GPM and natural computation algorithm has been developed and utilized to solve multiphase return trajectory optimization problem, where a phase is defined as a subinterval in which the right-hand side of the differential equation is continuous. GPM converts the optimal control problem to a nonlinear programming problem (NLP, which helps to improve calculation accuracy and speed of natural computation algorithm. Through numerical simulations, it is found that the multiphase optimal control problem could be solved perfectly.

Mirror hybrid reactor optimization studies

International Nuclear Information System (INIS)

Bender, D.J.

1976-01-01

A system model of the mirror hybrid reactor has been developed. The major components of the model include (1) the reactor description, (2) a capital cost analysis, (3) various fuel management schemes, and (4) an economic analysis that includes the hybrid plus its associated fission burner reactors. The results presented describe the optimization of the mirror hybrid reactor, the objective being to minimize the cost of electricity from the hybrid fission-burner reactor complex. We have examined hybrid reactors with two types of blankets, one containing natural uranium, the other thorium. The major difference between the two optimized reactors is that the uranium hybrid is a significant net electrical power producer, whereas the thorium hybrid just about breaks even on electrical power. Our projected costs for fissile fuel production are approximately 50 $/g for 239 Pu and approximately 125 $/g for 233 U

Optimal control of a repowered vehicle: Plug-in fuel cell against plug-in hybrid electric powertrain

Energy Technology Data Exchange (ETDEWEB)

Tribioli, L., E-mail: laura.tribioli@unicusano.it; Cozzolino, R. [Dept. of Industrial Engineering, University of Rome Niccolo’ Cusano (Italy); Barbieri, M. [Engineering Dept., University of Naples Parthenope, Centro Direzionale-Isola C4, 80143 Naples (Italy)

2015-03-10

This paper describes two different powertrain configurations for the repowering of a conventional vehicle, equipped with an internal combustion engine (ICE). A model of a mid-sized ICE-vehicle is realized and then modified to model both a parallel plug-in hybrid electric powertrain and a proton electrolyte membrane (PEM) fuel cell (FC) hybrid powertrain. The vehicle behavior under the application of an optimal control algorithm for the energy management is analyzed for the different scenarios and results are compared.

Optimal control of a repowered vehicle: Plug-in fuel cell against plug-in hybrid electric powertrain

International Nuclear Information System (INIS)

Tribioli, L.; Cozzolino, R.; Barbieri, M.

2015-01-01

This paper describes two different powertrain configurations for the repowering of a conventional vehicle, equipped with an internal combustion engine (ICE). A model of a mid-sized ICE-vehicle is realized and then modified to model both a parallel plug-in hybrid electric powertrain and a proton electrolyte membrane (PEM) fuel cell (FC) hybrid powertrain. The vehicle behavior under the application of an optimal control algorithm for the energy management is analyzed for the different scenarios and results are compared

Development of an Optimal Power Control Scheme for Wave-Offshore Hybrid Generation Systems

Directory of Open Access Journals (Sweden)

Seungmin Jung

2015-08-01

Full Text Available Integration technology of various distribution systems for improving renewable energy utilization has been receiving attention in the power system industry. The wave-offshore hybrid generation system (HGS, which has a capacity of over 10 MW, was recently developed by adopting several voltage source converters (VSC, while a control method for adopted power conversion systems has not yet been configured in spite of the unique system characteristics of the designated structure. This paper deals with a reactive power assignment method for the developed hybrid system to improve the power transfer efficiency of the entire system. Through the development and application processes for an optimization algorithm utilizing the real-time active power profiles of each generator, a feasibility confirmation of power transmission loss reduction was implemented. To find the practical effect of the proposed control scheme, the real system information regarding the demonstration process was applied from case studies. Also, an evaluation for the loss of the improvement rate was calculated.

Hybrid Predictive Control for Dynamic Transport Problems

CERN Document Server

Núñez, Alfredo A; Cortés, Cristián E

2013-01-01

Hybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ●hybrid predictive control (HPC) ...

Hybrid cryptosystem RSA - CRT optimization and VMPC

Science.gov (United States)

Rahmadani, R.; Mawengkang, H.; Sutarman

2018-03-01

Hybrid cryptosystem combines symmetric algorithms and asymmetric algorithms. This combination utilizes speeds on encryption/decryption processes of symmetric algorithms and asymmetric algorithms to secure symmetric keys. In this paper we propose hybrid cryptosystem that combine symmetric algorithms VMPC and asymmetric algorithms RSA - CRT optimization. RSA - CRT optimization speeds up the decryption process by obtaining plaintext with dp and p key only, so there is no need to perform CRT processes. The VMPC algorithm is more efficient in software implementation and reduces known weaknesses in RC4 key generation. The results show hybrid cryptosystem RSA - CRT optimization and VMPC is faster than hybrid cryptosystem RSA - VMPC and hybrid cryptosystem RSA - CRT - VMPC. Keyword : Cryptography, RSA, RSA - CRT, VMPC, Hybrid Cryptosystem.

Power control based on particle swarm optimization of grid-connected inverter for hybrid renewable energy system

International Nuclear Information System (INIS)

García-Triviño, Pablo; Gil-Mena, Antonio José; Llorens-Iborra, Francisco; García-Vázquez, Carlos Andrés; Fernández-Ramírez, Luis M.; Jurado, Francisco

2015-01-01

Highlights: • Three PSO-based PI controllers for a grid-connected inverter were presented. • Two online PSO-based PI controllers were compared with an offline PSO-tuned PI. • The HRES and the inverter were evaluated under power changes and grid voltage sags. • Online ITAE-based PSO reduced ITAE (current THD) by 15.24% (5.32%) versus offline one. - Abstract: This paper is focused on the study of particle swarm optimization (PSO)-based PI controllers for the power control of a grid-connected inverter supplied from a hybrid renewable energy system. It is composed of two renewable energy sources (wind turbine and photovoltaic – PV – solar panels) and two energy storage systems (battery and hydrogen system, integrated by fuel cell and electrolyzer). Three PSO-based PI controllers are implemented: (1) conventional PI controller with offline tuning by PSO algorithm based on the integral time absolute error (ITAE) index; (2) PI controllers with online self-tuning by PSO algorithm based on the error; and (3) PI controllers with online self-tuning by PSO algorithm based on the ITAE index. To evaluate and compare the three controllers, the hybrid renewable energy system and the grid-connected inverter are simulated under changes in the active and reactive power values, as well as under a grid voltage sag. The results show that the online PSO-based PI controllers that optimize the ITAE index achieves the best response

Optimization of hybrid system (wind-solar energy) for pumping water ...

African Journals Online (AJOL)

This paper presents an optimization method for a hybrid (wind-solar) autonomous system designed for pumping water. This method is based on mathematical models demonstrated for the analysis and control of the performance of the various components of the hybrid system. These models provide an estimate of ...

Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time.

Science.gov (United States)

Das, Saptarshi; Pan, Indranil; Das, Shantanu

2013-07-01

Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Directory of Open Access Journals (Sweden)

Narinder Singh

2018-03-01

Full Text Available The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO and Whale Optimizer Algorithm (WOA. We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO algorithm: (i firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

Optimal control of hybrid qubits: Implementing the quantum permutation algorithm

Science.gov (United States)

Rivera-Ruiz, C. M.; de Lima, E. F.; Fanchini, F. F.; Lopez-Richard, V.; Castelano, L. K.

2018-03-01

The optimal quantum control theory is employed to determine electric pulses capable of producing quantum gates with a fidelity higher than 0.9997, when noise is not taken into account. Particularly, these quantum gates were chosen to perform the permutation algorithm in hybrid qubits in double quantum dots (DQDs). The permutation algorithm is an oracle based quantum algorithm that solves the problem of the permutation parity faster than a classical algorithm without the necessity of entanglement between particles. The only requirement for achieving the speedup is the use of a one-particle quantum system with at least three levels. The high fidelity found in our results is closely related to the quantum speed limit, which is a measure of how fast a quantum state can be manipulated. Furthermore, we model charge noise by considering an average over the optimal field centered at different values of the reference detuning, which follows a Gaussian distribution. When the Gaussian spread is of the order of 5 μ eV (10% of the correct value), the fidelity is still higher than 0.95. Our scheme also can be used for the practical realization of different quantum algorithms in DQDs.

PEMFC Optimization Strategy with Auxiliary Power Source in Fuel Cell Hybrid Vehicle

Directory of Open Access Journals (Sweden)

Tinton Dwi Atmaja

2012-02-01

Full Text Available Page HeaderOpen Journal SystemsJournal HelpUser You are logged in as...aulia My Journals My Profile Log Out Log Out as UserNotifications View (27 new ManageJournal Content SearchBrowse By Issue By Author By Title Other JournalsFont SizeMake font size smaller Make font size default Make font size largerInformation For Readers For Authors For LibrariansKeywords CBPNN Displacement FLC LQG/LTR Mixed PMA Ventilation bottom shear stress direct multiple shooting effective fuzzy logic geoelectrical method hourly irregular wave missile trajectory panoramic image predator-prey systems seawater intrusion segmentation structure development pattern terminal bunt manoeuvre Home About User Home Search Current Archives ##Editorial Board##Home > Vol 23, No 1 (2012 > AtmajaPEMFC Optimization Strategy with Auxiliary Power Source in Fuel Cell Hybrid VehicleTinton Dwi Atmaja, Amin AminAbstractone of the present-day implementation of fuel cell is acting as main power source in Fuel Cell Hybrid Vehicle (FCHV. This paper proposes some strategies to optimize the performance of Polymer Electrolyte Membrane Fuel Cell (PEMFC implanted with auxiliary power source to construct a proper FCHV hybridization. The strategies consist of the most updated optimization method determined from three point of view i.e. Energy Storage System (ESS, hybridization topology and control system analysis. The goal of these strategies is to achieve an optimum hybridization with long lifetime, low cost, high efficiency, and hydrogen consumption rate improvement. The energy storage system strategy considers battery, supercapacitor, and high-speed flywheel as the most promising alternative auxiliary power source. The hybridization topology strategy analyzes the using of multiple storage devices injected with electronic components to bear a higher fuel economy and cost saving. The control system strategy employs nonlinear control system to optimize the ripple factor of the voltage and the current

Original Framework for Optimizing Hybrid Energy Supply

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Amevi Acakpovi

2016-01-01

Full Text Available This paper proposes an original framework for optimizing hybrid energy systems. The recent growth of hybrid energy systems in remote areas across the world added to the increasing cost of renewable energy has triggered the inevitable development of hybrid energy systems. Hybrid energy systems always pose a problem of optimization of cost which has been approached with different perspectives in the recent past. This paper proposes a framework to guide the techniques of optimizing hybrid energy systems in general. The proposed framework comprises four stages including identification of input variables for energy generation, establishment of models of energy generation by individual sources, development of artificial intelligence, and finally summation of selected sources. A case study of a solar, wind, and hydro hybrid system was undertaken with a linear programming approach. Substantial results were obtained with regard to how load requests were constantly satisfied while minimizing the cost of electricity. The developed framework gained its originality from the fact that it has included models of individual sources of energy that even make the optimization problem more complex. This paper also has impacts on the development of policies which will encourage the integration and development of renewable energies.

Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles

NARCIS (Netherlands)

Silvas, E.; Hofman, T.; Murgovski, N.; Etman, L.F.P.; Steinbuch, M.

2017-01-01

The optimal design of a hybrid electric vehicle (HEV) can be formulated as a multiobjective optimization problem that spreads over multiple levels (technology, topology, size, and control). In the last decade, studies have shown that by integrating these optimization levels, fuel benefits are

Hybrid Optimization in the Design of Reciprocal Structures

DEFF Research Database (Denmark)

Parigi, Dario; Kirkegaard, Poul Henning; Sassone, Mario

2012-01-01

that explore the global domain of solutions as genetic algorithms (GAs). The benchmark tests show that when the control on the topology is required the best result is obtained by a hybrid approach that combines the global search of the GA with the local search of a GB algorithm. The optimization method......The paper presents a method to generate the geometry of reciprocal structures by means of a hybrid optimization procedure. The geometry of reciprocal structures where elements are sitting on the top or in the bottom of each other is extremely difficult to predict because of the non....... In this paper it is shown that the geometrically compatible position of the elements could be determined by local search algorithm gradient-based (GB). However the control on which bar sit on the top or in the bottom at each connection can be regarded as a topological problem and require the use of algorithms...

Hybrid Recurrent Laguerre-Orthogonal-Polynomial NN Control System Applied in V-Belt Continuously Variable Transmission System Using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Chih-Hong Lin

2015-01-01

Full Text Available Because the V-belt continuously variable transmission (CVT system driven by permanent magnet synchronous motor (PMSM has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming procedure. In order to overcome difficulties for design of the linear controllers, the hybrid recurrent Laguerre-orthogonal-polynomial neural network (NN control system which has online learning ability to respond to the system’s nonlinear and time-varying behaviors is proposed to control PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Laguerre-orthogonal-polynomial NN control system consists of an inspector control, a recurrent Laguerre-orthogonal-polynomial NN control with adaptive law, and a recouped control with estimated law. Moreover, the adaptive law of online parameters in the recurrent Laguerre-orthogonal-polynomial NN is derived using the Lyapunov stability theorem. Furthermore, the optimal learning rate of the parameters by means of modified particle swarm optimization (PSO is proposed to achieve fast convergence. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

Review of optimization strategies for system-level design in hybrid electric vehicles

NARCIS (Netherlands)

Silvas, E.; Hofman, T.; Murgovski, N.; Etman, P.; Steinbuch, M.

2017-01-01

The optimal design of a hybrid electric vehicle can be formulated as a multi-objective optimization problem that spreads over multiple levels (technology, topology, size and control). In the last decade, studies have shown that, by integrating these optimization levels fuel benefits are obtained,

Operations Optimization of Hybrid Energy Systems under Variable Markets

Energy Technology Data Exchange (ETDEWEB)

Chen, Jun; Garcia, Humberto E.

2016-07-01

Hybrid energy systems (HES) have been proposed to be an important element to enable increasing penetration of clean energy. This paper investigates the operations flexibility of HES, and develops a methodology for operations optimization to maximize its economic value based on predicted renewable generation and market information. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value, and is illustrated by numerical results.

A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

Directory of Open Access Journals (Sweden)

Daqing Wu

2012-01-01

Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

Simultaneous Optimization of Topology and Component Sizes for Double Planetary Gear Hybrid Powertrains

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Weichao Zhuang

2016-05-01

Full Text Available Hybrid powertrain technologies are successful in the passenger car market and have been actively developed in recent years. Optimal topology selection, component sizing, and controls are required for competitive hybrid vehicles, as multiple goals must be considered simultaneously: fuel efficiency, emissions, performance, and cost. Most of the previous studies explored these three design dimensions separately. In this paper, two novel frameworks combining these three design dimensions together are presented and compared. One approach is nested optimization which searches through the whole design space exhaustively. The second approach is called enhanced iterative optimization, which executes the topology optimization and component sizing alternately. A case study shows that the later method can converge to the global optimal design generated from the nested optimization, and is much more computationally efficient. In addition, we also address a known issue of optimal designs: their sensitivity to parameters, such as varying vehicle weight, which is a concern especially for the design of hybrid buses. Therefore, the iterative optimization process is applied to design a robust multi-mode hybrid electric bus under different loading scenarios as the final design challenge of this paper.

Optimal Lunar Landing Trajectory Design for Hybrid Engine

Directory of Open Access Journals (Sweden)

Dong-Hyun Cho

2015-01-01

Full Text Available The lunar landing stage is usually divided into two parts: deorbit burn and powered descent phases. The optimal lunar landing problem is likely to be transformed to the trajectory design problem on the powered descent phase by using continuous thrusters. The optimal lunar landing trajectories in general have variety in shape, and the lunar lander frequently increases its altitude at the initial time to obtain enough time to reduce the horizontal velocity. Due to the increment in the altitude, the lunar lander requires more fuel for lunar landing missions. In this work, a hybrid engine for the lunar landing mission is introduced, and an optimal lunar landing strategy for the hybrid engine is suggested. For this approach, it is assumed that the lunar lander retrofired the impulsive thruster to reduce the horizontal velocity rapidly at the initiated time on the powered descent phase. Then, the lunar lander reduced the total velocity and altitude for the lunar landing by using the continuous thruster. In contradistinction to other formal optimal lunar landing problems, the initial horizontal velocity and mass are not fixed at the start time. The initial free optimal control theory is applied, and the optimal initial value and lunar landing trajectory are obtained by simulation studies.

Application of Hybrid Optimization Algorithm in the Synthesis of Linear Antenna Array

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Ezgi Deniz Ülker

2014-01-01

Full Text Available The use of hybrid algorithms for solving real-world optimization problems has become popular since their solution quality can be made better than the algorithms that form them by combining their desirable features. The newly proposed hybrid method which is called Hybrid Differential, Particle, and Harmony (HDPH algorithm is different from the other hybrid forms since it uses all features of merged algorithms in order to perform efficiently for a wide variety of problems. In the proposed algorithm the control parameters are randomized which makes its implementation easy and provides a fast response. This paper describes the application of HDPH algorithm to linear antenna array synthesis. The results obtained with the HDPH algorithm are compared with three merged optimization techniques that are used in HDPH. The comparison shows that the performance of the proposed algorithm is comparatively better in both solution quality and robustness. The proposed hybrid algorithm HDPH can be an efficient candidate for real-time optimization problems since it yields reliable performance at all times when it gets executed.

Analysis and design of hybrid control systems

Energy Technology Data Exchange (ETDEWEB)

Malmborg, J.

1998-05-01

Different aspects of hybrid control systems are treated: analysis, simulation, design and implementation. A systematic methodology using extended Lyapunov theory for design of hybrid systems is developed. The methodology is based on conventional control designs in separate regions together with a switching strategy. Dynamics are not well defined if the control design methods lead to fast mode switching. The dynamics depend on the salient features of the implementation of the mode switches. A theorem for the stability of second order switching together with the resulting dynamics is derived. The dynamics on an intersection of two sliding sets are defined for two relays working on different time scales. The current simulation packages have problems modeling and simulating hybrid systems. It is shown how fast mode switches can be found before or during simulation. The necessary analysis work is a very small overhead for a modern simulation tool. To get some experience from practical problems with hybrid control the switching strategy is implemented in two different software environments. In one of them a time-optimal controller is added to an existing PID controller on a commercial control system. Successful experiments with this hybrid controller shows the practical use of the method 78 refs, 51 figs, 2 tabs

Optimization-based power management of hybrid power systems with applications in advanced hybrid electric vehicles and wind farms with battery storage

Science.gov (United States)

Borhan, Hoseinali

Modern hybrid electric vehicles and many stationary renewable power generation systems combine multiple power generating and energy storage devices to achieve an overall system-level efficiency and flexibility which is higher than their individual components. The power or energy management control, "brain" of these "hybrid" systems, determines adaptively and based on the power demand the power split between multiple subsystems and plays a critical role in overall system-level efficiency. This dissertation proposes that a receding horizon optimal control (aka Model Predictive Control) approach can be a natural and systematic framework for formulating this type of power management controls. More importantly the dissertation develops new results based on the classical theory of optimal control that allow solving the resulting optimal control problem in real-time, in spite of the complexities that arise due to several system nonlinearities and constraints. The dissertation focus is on two classes of hybrid systems: hybrid electric vehicles in the first part and wind farms with battery storage in the second part. The first part of the dissertation proposes and fully develops a real-time optimization-based power management strategy for hybrid electric vehicles. Current industry practice uses rule-based control techniques with "else-then-if" logic and look-up maps and tables in the power management of production hybrid vehicles. These algorithms are not guaranteed to result in the best possible fuel economy and there exists a gap between their performance and a minimum possible fuel economy benchmark. Furthermore, considerable time and effort are spent calibrating the control system in the vehicle development phase, and there is little flexibility in real-time handling of constraints and re-optimization of the system operation in the event of changing operating conditions and varying parameters. In addition, a proliferation of different powertrain configurations may

Risk-sensitive control of stochastic hybrid systems on infinite time horizon

Directory of Open Access Journals (Sweden)

Runolfsson Thordur

1999-01-01

Full Text Available A risk-sensitive optimal control problem is considered for a hybrid system that consists of continuous time diffusion process that depends on a discrete valued mode variable that is modeled as a Markov chain. Optimality conditions are presented and conditions for the existence of optimal controls are derived. It is shown that the optimal risk-sensitive control problem is equivalent to the upper value of an associated stochastic differential game, and insight into the contributions of the noise input and mode variable to the risk sensitivity of the cost functional is given. Furthermore, it is shown that due to the mode variable risk sensitivity, the equivalence relationship that has been observed between risk-sensitive and H ∞ control in the nonhybrid case does not hold for stochastic hybrid systems.

A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

International Nuclear Information System (INIS)

Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge

2014-01-01

Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily

An integrated optimization approach for a hybrid energy system in electric vehicles

International Nuclear Information System (INIS)

Hung, Yi-Hsuan; Wu, Chien-Hsun

2012-01-01

Highlights: ► Second-order control-oriented dynamics for a battery/supercapacitor EV is modeled. ► Multiple for-loop programming and global searchwith constraints are main design principles of integrated optimization algorithm (IOA). ► Optimal hybridization is derived based on maximizing energy storage capacity. ► Optimal energy management in three EV operation modes is searched based on minimizing total consumed power. ► Simulation results prove that 6+% of total energy is saved by the IOA method. -- Abstract: This paper develops a simple but innovative integrated optimization approach (IOA) for deriving the best solutions of component sizing and control strategies of a hybrid energy system (HES) which consists of a lithium battery and a supercapacitor module. To implement IOA, a multiple for-loop structure with a preset cost function is needed to globally calculate the best hybridization and energy management of the HES. For system hybridization, the optimal size ratio is evaluated by maximizing the HES energy stored capacity at various costs. For energy management, the optimal power distribution combined with a three-mode rule-based strategy is searched to minimize the total consumed energy. Combining above two for-loop structures and giving a time-dependent test scenario, the IOA is derived by minimizing the accumulated HES power. Simulation results show that 6% of the total HES energy can be saved in the IOA case compared with the original system in two driving cycles: ECE and UDDS, and two vehicle weights, respectively. It proves that the IOA effectively derives the maximum energy storage capacity and the minimum energy consumption of the HES at the same time. Experimental verification will be carried out in the near future.

Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System

Directory of Open Access Journals (Sweden)

Farouk Odeim

2015-06-01

Full Text Available In this paper, an experimental fuel cell/battery/supercapacitor hybrid system is investigated in terms of modeling and power management design and optimization. The power management strategy is designed based on the role that should be played by each component of the hybrid power source. The supercapacitor is responsible for the peak power demands. The battery assists the supercapacitor in fulfilling the transient power demand by controlling its state-of-energy, whereas the fuel cell system, with its slow dynamics, controls the state-of-charge of the battery. The parameters of the power management strategy are optimized by a genetic algorithm and Pareto front analysis in a framework of multi-objective optimization, taking into account the hydrogen consumption, the battery loading and the acceleration performance. The optimization results are validated on a test bench composed of a fuel cell system (1.2 kW, 26 V, lithium polymer battery (30 Ah, 37 V, and a supercapacitor (167 F, 48 V.

Optimal design of a hybridization scheme with a fuel cell using genetic optimization

Science.gov (United States)

Rodriguez, Marco A.

Fuel cell is one of the most dependable "green power" technologies, readily available for immediate application. It enables direct conversion of hydrogen and other gases into electric energy without any pollution of the environment. However, the efficient power generation is strictly stationary process that cannot operate under dynamic environment. Consequently, fuel cell becomes practical only within a specially designed hybridization scheme, capable of power storage and power management functions. The resultant technology could be utilized to its full potential only when both the fuel cell element and the entire hybridization scheme are optimally designed. The design optimization in engineering is among the most complex computational tasks due to its multidimensionality, nonlinearity, discontinuity and presence of constraints in the underlying optimization problem. this research aims at the optimal utilization of the fuel cell technology through the use of genetic optimization, and advance computing. This study implements genetic optimization in the definition of optimum hybridization rules for a PEM fuel cell/supercapacitor power system. PEM fuel cells exhibit high energy density but they are not intended for pulsating power draw applications. They work better in steady state operation and thus, are often hybridized. In a hybrid system, the fuel cell provides power during steady state operation while capacitors or batteries augment the power of the fuel cell during power surges. Capacitors and batteries can also be recharged when the motor is acting as a generator. Making analogies to driving cycles, three hybrid system operating modes are investigated: 'Flat' mode, 'Uphill' mode, and 'Downhill' mode. In the process of discovering the switching rules for these three modes, we also generate a model of a 30W PEM fuel cell. This study also proposes the optimum design of a 30W PEM fuel cell. The PEM fuel cell model and hybridization's switching rules are postulated

Aerodynamic Shape Optimization Using Hybridized Differential Evolution

Science.gov (United States)

Madavan, Nateri K.

2003-01-01

An aerodynamic shape optimization method that uses an evolutionary algorithm known at Differential Evolution (DE) in conjunction with various hybridization strategies is described. DE is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Various hybridization strategies for DE are explored, including the use of neural networks as well as traditional local search methods. A Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the hybrid DE optimizer. The method is implemented on distributed parallel computers so that new designs can be obtained within reasonable turnaround times. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. (The final paper will include at least one other aerodynamic design application). The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated.

Optimal control of a fuel cell/wind/PV/grid hybrid system with thermal heat pump load

CSIR Research Space (South Africa)

Sichilalu, S

2016-10-01

Full Text Available This paper presents an optimal energy management strategy for a grid-tied photovoltaic–wind-fuel cell hybrid power supply system. The hybrid system meets the load demand consisting of an electrical load and a heat pump water heater supplying thermal...

Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs

Directory of Open Access Journals (Sweden)

Jiajun Liu

2017-10-01

Full Text Available Energy storage systems (ESS play an important role in the performance of mining vehicles. A hybrid ESS combining both batteries (BTs and supercapacitors (SCs is one of the most promising solutions. As a case study, this paper discusses the optimal hybrid ESS sizing and energy management strategy (EMS of 14-ton underground load-haul-dump vehicles (LHDs. Three novel contributions are added to the relevant literature. First, a multi-objective optimization is formulated regarding energy consumption and the total cost of a hybrid ESS, which are the key factors of LHDs, and a battery capacity degradation model is used. During the process, dynamic programming (DP-based EMS is employed to obtain the optimal energy consumption and hybrid ESS power profiles. Second, a 10-year life cycle cost model of a hybrid ESS for LHDs is established to calculate the total cost, including capital cost, operating cost, and replacement cost. According to the optimization results, three solutions chosen from the Pareto front are compared comprehensively, and the optimal one is selected. Finally, the optimal and battery-only options are compared quantitatively using the same objectives, and the hybrid ESS is found to be a more economical and efficient option.

Energy Optimization for a Weak Hybrid Power System of an Automobile Exhaust Thermoelectric Generator

Science.gov (United States)

Fang, Wei; Quan, Shuhai; Xie, Changjun; Tang, Xinfeng; Ran, Bin; Jiao, Yatian

2017-11-01

An integrated starter generator (ISG)-type hybrid electric vehicle (HEV) scheme is proposed based on the automobile exhaust thermoelectric generator (AETEG). An eddy current dynamometer is used to simulate the vehicle's dynamic cycle. A weak ISG hybrid bench test system is constructed to test the 48 V output from the power supply system, which is based on engine exhaust-based heat power generation. The thermoelectric power generation-based system must ultimately be tested when integrated into the ISG weak hybrid mixed power system. The test process is divided into two steps: comprehensive simulation and vehicle-based testing. The system's dynamic process is simulated for both conventional and thermoelectric powers, and the dynamic running process comprises four stages: starting, acceleration, cruising and braking. The quantity of fuel available and battery pack energy, which are used as target vehicle energy functions for comparison with conventional systems, are simplified into a single energy target function, and the battery pack's output current is used as the control variable in the thermoelectric hybrid energy optimization model. The system's optimal battery pack output current function is resolved when its dynamic operating process is considered as part of the hybrid thermoelectric power generation system. In the experiments, the system bench is tested using conventional power and hybrid thermoelectric power for the four dynamic operation stages. The optimal battery pack curve is calculated by functional analysis. In the vehicle, a power control unit is used to control the battery pack's output current and minimize energy consumption. Data analysis shows that the fuel economy of the hybrid power system under European Driving Cycle conditions is improved by 14.7% when compared with conventional systems.

A hybrid iterative scheme for optimal control problems governed by ...

African Journals Online (AJOL)

MRT

KEY WORDS: Optimal control problem; Fredholm integral equation; ... control problems governed by Fredholm integral and integro-differential equations is given in (Brunner and Yan, ..... The exact optimal trajectory and control functions are. 2.

Active Vibration Control of Plate Partly Treated with ACLD Using Hybrid Control

Directory of Open Access Journals (Sweden)

Dongdong Zhang

2014-01-01

Full Text Available A finite element model of plate partly treated with ACLD treatments is developed based on the constitutive equations of elastic, piezoelectric, viscoelastic materials and Hamilton’s principle. The Golla-Hughes-Mctavish (GHM method is employed to describe the frequency-dependent characteristics of viscoelastic material (VEM. A model reduction is completed by using iterative dynamic condensation and balance model reduction method to design an effective control system. The emphasis is concerned on hybrid (combined feedback/feedforward control system to attenuate the vibration of plates with ACLD treatments. The optimal linear quadratic Gaussian (LQG controller is considered as a feedback channel and the adaptive filtered-reference LMS (FxLMS controller is used as a feedforward channel. They can be utilized individually or in a hybrid way to suppress the vibration of plate/ACLD system. The results show that the hybrid controller which combines feedback/feedforward together can reduce the displacement amplitude of plate/ACLD system subjected to a complicated disturbance substantially without requiring more control effort. Furthermore, the hybrid controller has more rapid and stable convergence rate than the adaptive feedforward FxLMS controller. Meanwhile, perfect robustness to phase error of the cancellation path in feedforward controller and the weight matrices in feedback LQG controller is demonstrated in proposed hybrid controller. Therefore, its application in structural engineering can be highly appreciated.

Real Time Energy Management Control Strategies for Hybrid Powertrains

Science.gov (United States)

Zaher, Mohamed Hegazi Mohamed

In order to improve fuel efficiency and reduce emissions of mobile vehicles, various hybrid power-train concepts have been developed over the years. This thesis focuses on embedded control of hybrid powertrain concepts for mobile vehicle applications. Optimal robust control approach is used to develop a real time energy management strategy for continuous operations. The main idea is to store the normally wasted mechanical regenerative energy in energy storage devices for later usage. The regenerative energy recovery opportunity exists in any condition where the speed of motion is in opposite direction to the applied force or torque. This is the case when the vehicle is braking, decelerating, or the motion is driven by gravitational force, or load driven. There are three main concepts for regernerative energy storing devices in hybrid vehicles: electric, hydraulic, and flywheel. The real time control challenge is to balance the system power demand from the engine and the hybrid storage device, without depleting the energy storage device or stalling the engine in any work cycle, while making optimal use of the energy saving opportunities in a given operational, often repetitive cycle. In the worst case scenario, only engine is used and hybrid system completely disabled. A rule based control is developed and tuned for different work cycles and linked to a gain scheduling algorithm. A gain scheduling algorithm identifies the cycle being performed by the machine and its position via GPS, and maps them to the gains.

Global Optimization Based on the Hybridization of Harmony Search and Particle Swarm Optimization Methods

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2014-01-01

Full Text Available We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS and swarm of particles (PSO, software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called PSO HS , present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.

Control of equipment isolation system using wavelet-based hybrid sliding mode control

Science.gov (United States)

Huang, Shieh-Kung; Loh, Chin-Hsiung

2017-04-01

Critical non-structural equipment, including life-saving equipment in hospitals, circuit breakers, computers, high technology instrumentations, etc., is vulnerable to strong earthquakes, and on top of that, the failure of the vibration-sensitive equipment will cause severe economic loss. In order to protect vibration-sensitive equipment or machinery against strong earthquakes, various innovative control algorithms are developed to compensate the internal forces that to be applied. These new or improved control strategies, such as the control algorithms based on optimal control theory and sliding mode control (SMC), are also developed for structures engineering as a key element in smart structure technology. The optimal control theory, one of the most common methodologies in feedback control, finds control forces through achieving a certain optimal criterion by minimizing a cost function. For example, the linear-quadratic regulator (LQR) was the most popular control algorithm over the past three decades, and a number of modifications have been proposed to increase the efficiency of classical LQR algorithm. However, except to the advantage of simplicity and ease of implementation, LQR are susceptible to parameter uncertainty and modeling error due to complex nature of civil structures. Different from LQR control, a robust and easy to be implemented control algorithm, SMC has also been studied. SMC is a nonlinear control methodology that forces the structural system to slide along surfaces or boundaries; hence this control algorithm is naturally robust with respect to parametric uncertainties of a structure. Early attempts at protecting vibration-sensitive equipment were based on the use of existing control algorithms as described above. However, in recent years, researchers have tried to renew the existing control algorithms or developing a new control algorithm to adapt the complex nature of civil structures which include the control of both structures and non

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Directory of Open Access Journals (Sweden)

MadhuSudana Rao Nalluri

2017-01-01

Full Text Available With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM and multilayer perceptron (MLP technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs. Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

Optimal Design of a Novel Hybrid Electric Powertrain for Tracked Vehicles

Directory of Open Access Journals (Sweden)

Zhaobo Qin

2017-12-01

Full Text Available Tracked vehicles have been widely used in construction, agriculture, and the military. Major problems facing the industry, however, are high emissions and fuel consumption. Hybrid electric tracked vehicles have thus become increasingly popular because of their improved fuel economy and reduced emissions. While the series hybrid system has drawn the most attention and has been applied in most cases, the low efficiency caused by energy conversion losses and large propulsion motors has limited its development. A novel multi-mode powertrain with two output shafts controlling each side of the track independently is first proposed. The powertrain is a three-planetary-gear power-split system with one engine, three motors, and an ultracapacitor pack. Compared with the existing technologies, the proposed powertrain can realize skid steering without an extra steering mechanism, and significantly improve the overall efficiency. To demonstrate the advantages of the novel powertrain, a topology-control-size integrated optimization problem is solved based on drivability, fuel economy, and cost. Final simulation results show that the optimized design with downsized components can produce about a 30% improvement in drivability and a 15% improvement in fuel economy compared with the commonly used series hybrid benchmark. Moreover, the optimized design is verified to be much more economical taking cumulative cost into account, which is very attractive for potential industrial applications in the future.

Review of the Optimal Design on a Hybrid Renewable Energy System

Directory of Open Access Journals (Sweden)

Wu Yuan-Kang

2016-01-01

Full Text Available Hybrid renewable energy systems, combining various kinds of technologies, have shown relatively high capabilities to solve reliability problems and have reduced cost challenges. The use of hybrid electricity generation/storage technologies is reasonable to overcome related shortcomings. While the hybrid renewable energy system is attractive, its design, specifically the determination of the size of PV, wind, and diesel power generators and the size of energy storage system in each power station, is very challenging. Therefore, this paper will focus on the system planning and operation of hybrid generation systems, and several corresponding topics and papers by using intelligent computing methods will be reviewed. They include typical case studies, modeling and system simulation, control and management, reliability and economic studies, and optimal design on a reliable hybrid generation system.

Optimal scheduling for distributed hybrid system with pumped hydro storage

International Nuclear Information System (INIS)

Kusakana, Kanzumba

2016-01-01

Highlights: • Pumped hydro storage is proposed for isolated hybrid PV–Wind–Diesel systems. • Optimal control is developed to dispatch power flow economically. • A case study is conducted using the model for an isolated load. • Effects of seasons on the system’s optimal scheduling are examined through simulation. - Abstract: Photovoltaic and wind power generations are currently seen as sustainable options of in rural electrification, particularly in standalone applications. However the variable character of solar and wind resources as well as the variable load demand prevent these generation systems from being totally reliable without suitable energy storage system. Several research works have been conducted on the use of photovoltaic and wind systems in rural electrification; however most of these works have not considered other ways of storing energy except for conventional battery storage systems. In this paper, an energy dispatch model that satisfies the load demand, taking into account the intermittent nature of the solar and wind energy sources and variations in demand, is presented for a hybrid system consisting of a photovoltaic unit, a wind unit, a pumped hydro storage system and a diesel generator. The main purpose of the developed model is to minimize the hybrid system’s operation cost while optimizing the system’s power flow considering the different component’s operational constraints. The simulations have been performed using “fmincon” implemented in Matlab. The model have been applied to two test examples; the simulation results are analyzed and compared to the case where the diesel generator is used alone to supply the given load demand. The results show that using the developed control model for the proposed hybrid system, fuel saving can be achieved compared to the case where the diesel is used alone to supply the same load patters.

Arsenic removal from contaminated groundwater by membrane-integrated hybrid plant: optimization and control using Visual Basic platform.

Science.gov (United States)

Chakrabortty, S; Sen, M; Pal, P

2014-03-01

A simulation software (ARRPA) has been developed in Microsoft Visual Basic platform for optimization and control of a novel membrane-integrated arsenic separation plant in the backdrop of absence of such software. The user-friendly, menu-driven software is based on a dynamic linearized mathematical model, developed for the hybrid treatment scheme. The model captures the chemical kinetics in the pre-treating chemical reactor and the separation and transport phenomena involved in nanofiltration. The software has been validated through extensive experimental investigations. The agreement between the outputs from computer simulation program and the experimental findings are excellent and consistent under varying operating conditions reflecting high degree of accuracy and reliability of the software. High values of the overall correlation coefficient (R (2) = 0.989) and Willmott d-index (0.989) are indicators of the capability of the software in analyzing performance of the plant. The software permits pre-analysis, manipulation of input data, helps in optimization and exhibits performance of an integrated plant visually on a graphical platform. Performance analysis of the whole system as well as the individual units is possible using the tool. The software first of its kind in its domain and in the well-known Microsoft Excel environment is likely to be very useful in successful design, optimization and operation of an advanced hybrid treatment plant for removal of arsenic from contaminated groundwater.

Optimization methods applied to hybrid vehicle design

Science.gov (United States)

Donoghue, J. F.; Burghart, J. H.

1983-01-01

The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.

A "Hybrid" Approach for Synthesizing Optimal Controllers of Hybrid Systems

DEFF Research Database (Denmark)

Zhao, Hengjun; Zhan, Naijun; Kapur, Deepak

2012-01-01

to discretization manageable and within bounds. A major advantage of our approach is not only that it avoids errors due to numerical computation, but it also gives a better optimal controller. In order to illustrate our approach, we use the real industrial example of an oil pump provided by the German company HYDAC...

MODELLING AND CONTROL OF POWER-SPLIT HYBRID ELECTRIC VEHICLE USING FUZZY LOGIC METHOD

OpenAIRE

Mohammadpour, Ebrahim; Khajavi, Mehrdad Nouri

2014-01-01

Nowadays, automotive manufactures increasingly have lead to development of hybrid vehicles due to energy consumption growing and increased emissions. the power-split hybrids due to the simultaneous using of speed and torque couplings has integrated advantage of series and parallel hybrid systems and minimize their disadvantages , however the power-split hybrids control strategy is far more complex than other types. Generally the control strategy tries to use the optimize operating point of HE...

Fuel consumption optimization for smart hybrid electric vehicle during a car-following process

Science.gov (United States)

Li, Liang; Wang, Xiangyu; Song, Jian

2017-03-01

Hybrid electric vehicles (HEVs) provide large potential to save energy and reduce emission, and smart vehicles bring out great convenience and safety for drivers. By combining these two technologies, vehicles may achieve excellent performances in terms of dynamic, economy, environmental friendliness, safety, and comfort. Hence, a smart hybrid electric vehicle (s-HEV) is selected as a platform in this paper to study a car-following process with optimizing the fuel consumption. The whole process is a multi-objective optimal problem, whose optimal solution is not just adding an energy management strategy (EMS) to an adaptive cruise control (ACC), but a deep fusion of these two methods. The problem has more restricted conditions, optimal objectives, and system states, which may result in larger computing burden. Therefore, a novel fuel consumption optimization algorithm based on model predictive control (MPC) is proposed and some search skills are adopted in receding horizon optimization to reduce computing burden. Simulations are carried out and the results indicate that the fuel consumption of proposed method is lower than that of the ACC+EMS method on the condition of ensuring car-following performances.

Predictive control strategies for energy saving of hybrid electric vehicles based on traffic light information

Directory of Open Access Journals (Sweden)

Kaijiang YU

2015-10-01

Full Text Available As the conventional control method for hybrid electric vehicle doesn’t consider the effect of known traffic light information on the vehicle energy management, this paper proposes a model predictive control intelligent optimization strategies based on traffic light information for hybrid electric vehicles. By building the simplified model of the hybrid electric vehicle and adopting the continuation/generalized minimum residual method, the model prediction problem is solved. The simulation is conducted by using MATLAB/Simulink platform. The simulation results show the effectiveness of the proposed model of the traffic light information, and that the proposed model predictive control method can improve fuel economy and the real-time control performance significantly. The research conclusions show that the proposed control strategy can achieve optimal control of the vehicle trajectory, significantly improving fuel economy of the vehicle, and meet the system requirements for the real-time optimal control.

Optimal Battery Utilization Over Lifetime for Parallel Hybrid Electric Vehicle to Maximize Fuel Economy

Energy Technology Data Exchange (ETDEWEB)

Patil, Chinmaya; Naghshtabrizi, Payam; Verma, Rajeev; Tang, Zhijun; Smith, Kandler; Shi, Ying

2016-08-01

This paper presents a control strategy to maximize fuel economy of a parallel hybrid electric vehicle over a target life of the battery. Many approaches to maximizing fuel economy of parallel hybrid electric vehicle do not consider the effect of control strategy on the life of the battery. This leads to an oversized and underutilized battery. There is a trade-off between how aggressively to use and 'consume' the battery versus to use the engine and consume fuel. The proposed approach addresses this trade-off by exploiting the differences in the fast dynamics of vehicle power management and slow dynamics of battery aging. The control strategy is separated into two parts, (1) Predictive Battery Management (PBM), and (2) Predictive Power Management (PPM). PBM is the higher level control with slow update rate, e.g. once per month, responsible for generating optimal set points for PPM. The considered set points in this paper are the battery power limits and State Of Charge (SOC). The problem of finding the optimal set points over the target battery life that minimize engine fuel consumption is solved using dynamic programming. PPM is the lower level control with high update rate, e.g. a second, responsible for generating the optimal HEV energy management controls and is implemented using model predictive control approach. The PPM objective is to find the engine and battery power commands to achieve the best fuel economy given the battery power and SOC constraints imposed by PBM. Simulation results with a medium duty commercial hybrid electric vehicle and the proposed two-level hierarchical control strategy show that the HEV fuel economy is maximized while meeting a specified target battery life. On the other hand, the optimal unconstrained control strategy achieves marginally higher fuel economy, but fails to meet the target battery life.

Hybrid Firefly Variants Algorithm for Localization Optimization in WSN

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P. SrideviPonmalar

2017-01-01

Full Text Available Localization is one of the key issues in wireless sensor networks. Several algorithms and techniques have been introduced for localization. Localization is a procedural technique of estimating the sensor node location. In this paper, a novel three hybrid algorithms based on firefly is proposed for localization problem. Hybrid Genetic Algorithm-Firefly Localization Algorithm (GA-FFLA, Hybrid Differential Evolution-Firefly Localization Algorithm (DE-FFLA and Hybrid Particle Swarm Optimization -Firefly Localization Algorithm (PSO-FFLA are analyzed, designed and implemented to optimize the localization error. The localization algorithms are compared based on accuracy of estimation of location, time complexity and iterations required to achieve the accuracy. All the algorithms have hundred percent estimation accuracy but with variations in the number of firefliesr requirements, variation in time complexity and number of iteration requirements. Keywords: Localization; Genetic Algorithm; Differential Evolution; Particle Swarm Optimization

Centralized Stochastic Optimal Control of Complex Systems

Energy Technology Data Exchange (ETDEWEB)

Malikopoulos, Andreas [ORNL

2015-01-01

In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain using the long-run expected average cost per unit time criterion, and we show that the control policy yielding the Pareto optimal solution minimizes the average cost criterion online. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.

Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy

Directory of Open Access Journals (Sweden)

Maytham S. Ahmed

2016-09-01

Full Text Available Demand response (DR program can shift peak time load to off-peak time, thereby reducing greenhouse gas emissions and allowing energy conservation. In this study, the home energy management scheduling controller of the residential DR strategy is proposed using the hybrid lightning search algorithm (LSA-based artificial neural network (ANN to predict the optimal ON/OFF status for home appliances. Consequently, the scheduled operation of several appliances is improved in terms of cost savings. In the proposed approach, a set of the most common residential appliances are modeled, and their activation is controlled by the hybrid LSA-ANN based home energy management scheduling controller. Four appliances, namely, air conditioner, water heater, refrigerator, and washing machine (WM, are developed by Matlab/Simulink according to customer preferences and priority of appliances. The ANN controller has to be tuned properly using suitable learning rate value and number of nodes in the hidden layers to schedule the appliances optimally. Given that finding proper ANN tuning parameters is difficult, the LSA optimization is hybridized with ANN to improve the ANN performances by selecting the optimum values of neurons in each hidden layer and learning rate. Therefore, the ON/OFF estimation accuracy by ANN can be improved. Results of the hybrid LSA-ANN are compared with those of hybrid particle swarm optimization (PSO based ANN to validate the developed algorithm. Results show that the hybrid LSA-ANN outperforms the hybrid PSO based ANN. The proposed scheduling algorithm can significantly reduce the peak-hour energy consumption during the DR event by up to 9.7138% considering four appliances per 7-h period.

Robust Power Management Control for Stand-Alone Hybrid Power Generation System

International Nuclear Information System (INIS)

Kamal, Elkhatib; Adouane, Lounis; Aitouche, Abdel; Mohammed, Walaa

2017-01-01

This paper presents a new robust fuzzy control of energy management strategy for the stand-alone hybrid power systems. It consists of two levels named centralized fuzzy supervisory control which generates the power references for each decentralized robust fuzzy control. Hybrid power systems comprises: a photovoltaic panel and wind turbine as renewable sources, a micro turbine generator and a battery storage system. The proposed control strategy is able to satisfy the load requirements based on a fuzzy supervisor controller and manage power flows between the different energy sources and the storage unit by respecting the state of charge and the variation of wind speed and irradiance. Centralized controller is designed based on If-Then fuzzy rules to manage and optimize the hybrid power system production by generating the reference power for photovoltaic panel and wind turbine. Decentralized controller is based on the Takagi-Sugeno fuzzy model and permits us to stabilize each photovoltaic panel and wind turbine in presence of disturbances and parametric uncertainties and to optimize the tracking reference which is given by the centralized controller level. The sufficient conditions stability are formulated in the format of linear matrix inequalities using the Lyapunov stability theory. The effectiveness of the proposed Strategy is finally demonstrated through a SAHPS (stand-alone hybrid power systems) to illustrate the effectiveness of the overall proposed method. (paper)

Model Predictive Control for Connected Hybrid Electric Vehicles

Directory of Open Access Journals (Sweden)

Kaijiang Yu

2015-01-01

Full Text Available This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc. are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

Science.gov (United States)

Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

2010-01-01

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

Optimal control of mode transition for four-wheel-drive hybrid electric vehicle with dry dual-clutch transmission

Science.gov (United States)

Zhao, Zhiguo; Lei, Dan; Chen, Jiayi; Li, Hangyu

2018-05-01

When the four-wheel-drive hybrid electric vehicle (HEV) equipped with a dry dual clutch transmission (DCT) is in the mode transition process from pure electrical rear wheel drive to front wheel drive with engine or hybrid drive, the problem of vehicle longitudinal jerk is prominent. A mode transition robust control algorithm which resists external disturbance and model parameter fluctuation has been developed, by taking full advantage of fast and accurate torque (or speed) response of three electrical power sources and getting the clutch of DCT fully involved in the mode transition process. Firstly, models of key components of driveline system have been established, and the model of five-degrees-of-freedom vehicle longitudinal dynamics has been built by using a Uni-Tire model. Next, a multistage optimal control method has been produced to realize the decision of engine torque and clutch-transmitted torque. The sliding-mode control strategy for measurable disturbance has been proposed at the stage of engine speed dragged up. Meanwhile, the double tracking control architecture that integrates the model calculating feedforward control with H∞ robust feedback control has been presented at the stage of speed synchronization. Finally, the results from Matlab/Simulink software and hardware-in-the-loop test both demonstrate that the proposed control strategy for mode transition can not only coordinate the torque among different power sources and clutch while minimizing vehicle longitudinal jerk, but also provide strong robustness to model uncertainties and external disturbance.

Energy Optimal Control Strategy of PHEV Based on PMP Algorithm

Directory of Open Access Journals (Sweden)

Tiezhou Wu

2017-01-01

Full Text Available Under the global voice of “energy saving” and the current boom in the development of energy storage technology at home and abroad, energy optimal control of the whole hybrid electric vehicle power system, as one of the core technologies of electric vehicles, is bound to become a hot target of “clean energy” vehicle development and research. This paper considers the constraints to the performance of energy storage system in Parallel Hybrid Electric Vehicle (PHEV, from which lithium-ion battery frequently charges/discharges, PHEV largely consumes energy of fuel, and their are difficulty in energy recovery and other issues in a single cycle; the research uses lithium-ion battery combined with super-capacitor (SC, which is hybrid energy storage system (Li-SC HESS, working together with internal combustion engine (ICE to drive PHEV. Combined with PSO-PI controller and Li-SC HESS internal power limited management approach, the research proposes the PHEV energy optimal control strategy. It is based on revised Pontryagin’s minimum principle (PMP algorithm, which establishes the PHEV vehicle simulation model through ADVISOR software and verifies the effectiveness and feasibility. Finally, the results show that the energy optimization control strategy can improve the instantaneity of tracking PHEV minimum fuel consumption track, implement energy saving, and prolong the life of lithium-ion batteries and thereby can improve hybrid energy storage system performance.

Preliminary optimal configuration on free standing hybrid riser

Directory of Open Access Journals (Sweden)

Kyoung-Su Kim

2018-05-01

Full Text Available Free Standing Hybrid Riser (FSHR is comprised of vertical steel risers and Flexible Jumpers (FJ. They are jointly connected to a submerged Buoyancy Can (BC. There are several factors that have influence on the behavior of FSHR such as the span distance between an offshore platform and a foundation, BC up-lift force, BC submerged location and FJ length.An optimization method through a parametric study is presented. Firstly, descriptions for the overall arrangement and characteristics of FSHR are introduced. Secondly, a flowchart for optimization of FSHR is suggested. Following that, it is described how to select reasonable ranges for a parametric study and determine each of optimal configuration options. Lastly, numerical analysis based on this procedure is performed through a case study. In conclusion, the relation among those parameters is analyzed and non-dimensional parametric ranges on optimal arrangements are suggested. Additionally, strength analysis is performed with variation in the configuration. Keywords: Free standing hybrid riser, Hybrid riser system, Buoyancy can, Flexible jumper, Deepwater, Multi-body dynamics

Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

Directory of Open Access Journals (Sweden)

Hao Zhu

2017-04-01

Full Text Available Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters. This paper presents a hybrid uncertainty-based design optimization (UDO method developed from probability theory and interval theory. Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory, while the others are defined as interval variables with interval theory. Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncertainty propagation from the design parameters to system responses. Three design optimization strategies, including deterministic design optimization (DDO, probabilistic UDO and hybrid UDO, are applied to the conceptual design of a hybrid rocket motor (HRM used as the ascent propulsion system in Apollo lunar module. By comparison, the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.

Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

Institute of Scientific and Technical Information of China (English)

Zhu Hao; Tian Hui; Cai Guobiao

2017-01-01

Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters. This paper presents a hybrid uncertainty-based design optimization (UDO) method developed from probability theory and interval theory. Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory, while the others are defined as interval variables with interval theory. Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncer-tainty propagation from the design parameters to system responses. Three design optimization strategies, including deterministic design optimization (DDO), probabilistic UDO and hybrid UDO, are applied to the conceptual design of a hybrid rocket motor (HRM) used as the ascent propulsion system in Apollo lunar module. By comparison, the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.

Optimal Allocation of Power-Electronic Interfaced Wind Turbines Using a Genetic Algorithm - Monte Carlo Hybrid Optimization Method

DEFF Research Database (Denmark)

Chen, Peiyuan; Siano, Pierluigi; Chen, Zhe

2010-01-01

determined by the wind resource and geographic conditions, the location of wind turbines in a power system network may significantly affect the distribution of power flow, power losses, etc. Furthermore, modern WTs with power-electronic interface have the capability of controlling reactive power output...... limit requirements. The method combines the Genetic Algorithm (GA), gradient-based constrained nonlinear optimization algorithm and sequential Monte Carlo simulation (MCS). The GA searches for the optimal locations and capacities of WTs. The gradient-based optimization finds the optimal power factor...... setting of WTs. The sequential MCS takes into account the stochastic behaviour of wind power generation and load. The proposed hybrid optimization method is demonstrated on an 11 kV 69-bus distribution system....

Optimal Selective Harmonic Control for Power Harmonics Mitigation

DEFF Research Database (Denmark)

Zhou, Keliang; Yang, Yongheng; Blaabjerg, Frede

2015-01-01

of power harmonics. The proposed optimal SHC is of hybrid structure: all recursive SHC modules with weighted gains are connected in parallel. It bridges the real “nk+-m order RC” and the complex “parallel structure RC”. Compared to other IMP based control solutions, it offers an optimal trade-off among...

Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.

Science.gov (United States)

Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui

2006-01-01

This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.

GPAW optimized for Blue Gene/P using hybrid programming

DEFF Research Database (Denmark)

Kristensen, Mads Ruben Burgdorff; Happe, Hans Henrik; Vinter, Brian

2009-01-01

In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses on optimi......In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses...... on optimizing a very time consuming operation in GPAW, the finite-different stencil operation, and different hybrid programming approaches are evaluated. The work succeeds in demonstrating a hybrid programming model which is clearly beneficial compared to the original flat programming model. In total...... an improvement of 1.94 compared to the original implementation is obtained. The results we demonstrate here are reasonably general and may be applied to other finite difference codes....

Solution for state constrained optimal control problems applied to power split control for hybrid vehicles

NARCIS (Netherlands)

Keulen, van T.A.C.; Gillot, J.; Jager, de A.G.; Steinbuch, M.

2014-01-01

This paper presents a numerical solution for scalar state constrained optimal control problems. The algorithm rewrites the constrained optimal control problem as a sequence of unconstrained optimal control problems which can be solved recursively as a two point boundary value problem. The solution

Coordination of Heat Pumps, Electric Vehicles and AGC for Efficient LFC in a Smart Hybrid Power System via SCA-Based Optimized FOPID Controllers

Directory of Open Access Journals (Sweden)

Rahmat Khezri

2018-02-01

Full Text Available Due to the high price of fossil fuels, the increased carbon footprint in conventional generation units and the intermittent functionality of renewable units, alternative sources must contribute to the load frequency control (LFC of the power system. To tackle the challenge, dealing with controllable loads, the ongoing study aims at efficient LFC in smart hybrid power systems. To achieve this goal, heat pumps (HPs and electric vehicles (EVs are selected as the most effective controllable loads to contribute to the LFC issue. In this regard, the EVs can be controlled in a bidirectional manner as known charging and discharging states under a smart structure. In addition, regarding the HPs, the power consumption is controllable. As the main task, this paper proposes a fractional order proportional integral differential (FOPID controller for coordinated control of power consumption in HPs, the discharging state in EVs and automatic generation control (AGC. The parameters of the FOPID controllers are optimized simultaneously by the sine cosine algorithm (SCA, which is a new method for optimization problems. In the sequel, four scenarios, including step and random load changes, aggregated intermittent generated power from wind turbines, a random load change scenario and a sensitivity analysis scenario, are selected to demonstrate the efficiency of the proposed SCA-based FOPID controllers in a hybrid two-area power system.

Solution Approach to Automatic Generation Control Problem Using Hybridized Gravitational Search Algorithm Optimized PID and FOPID Controllers

Directory of Open Access Journals (Sweden)

DAHIYA, P.

2015-05-01

Full Text Available This paper presents the application of hybrid opposition based disruption operator in gravitational search algorithm (DOGSA to solve automatic generation control (AGC problem of four area hydro-thermal-gas interconnected power system. The proposed DOGSA approach combines the advantages of opposition based learning which enhances the speed of convergence and disruption operator which has the ability to further explore and exploit the search space of standard gravitational search algorithm (GSA. The addition of these two concepts to GSA increases its flexibility for solving the complex optimization problems. This paper addresses the design and performance analysis of DOGSA based proportional integral derivative (PID and fractional order proportional integral derivative (FOPID controllers for automatic generation control problem. The proposed approaches are demonstrated by comparing the results with the standard GSA, opposition learning based GSA (OGSA and disruption based GSA (DGSA. The sensitivity analysis is also carried out to study the robustness of DOGSA tuned controllers in order to accommodate variations in operating load conditions, tie-line synchronizing coefficient, time constants of governor and turbine. Further, the approaches are extended to a more realistic power system model by considering the physical constraints such as thermal turbine generation rate constraint, speed governor dead band and time delay.

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Directory of Open Access Journals (Sweden)

Narinder Singh

2017-01-01

Full Text Available A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO and Grey Wolf Optimizer (GWO. The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants’ strength. Some unimodal, multimodal, and fixed-dimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.

Solid Rocket Motor Design Using Hybrid Optimization

Directory of Open Access Journals (Sweden)

Kevin Albarado

2012-01-01

Full Text Available A particle swarm/pattern search hybrid optimizer was used to drive a solid rocket motor modeling code to an optimal solution. The solid motor code models tapered motor geometries using analytical burn back methods by slicing the grain into thin sections along the axial direction. Grains with circular perforated stars, wagon wheels, and dog bones can be considered and multiple tapered sections can be constructed. The hybrid approach to optimization is capable of exploring large areas of the solution space through particle swarming, but is also able to climb “hills” of optimality through gradient based pattern searching. A preliminary method for designing tapered internal geometry as well as tapered outer mold-line geometry is presented. A total of four optimization cases were performed. The first two case studies examines designing motors to match a given regressive-progressive-regressive burn profile. The third case study studies designing a neutrally burning right circular perforated grain (utilizing inner and external geometry tapering. The final case study studies designing a linearly regressive burning profile for right circular perforated (tapered grains.

A control-oriented simulation model of a power-split hybrid electric vehicle

International Nuclear Information System (INIS)

Cipek, Mihael; Pavković, Danijel; Petrić, Joško

2013-01-01

Highlights: ► A simulation model of a two mode power-split hybrid electric vehicle (HEV) is proposed. ► Modeling the energy losses in the HEV transmission components are presented. ► The control optimization model implementation aspects are discussed. -- Abstract: A simulation model of a two mode power-split hybrid electric vehicle (HEV) is proposed in this paper for the purpose of HEV dynamics analysis and control system design. The bond graph methodology is used to model dominant dynamic effects of the mechanical part of the HEV transmission. Simple quasi-static battery model, the environment model, the tire and the power losses model of a vehicle are included, as well. A low-level electric generator speed control loop is designed, which includes a PI controller tuned according to the symmetrical optimum tuning procedure. Finally, off-line optimization by conjugate gradient-based BPTT-like optimal control algorithm, which is based on the presented mathematical model, is also given in the paper.

Optimizing energy management of fuel cell-direct storage-hybrid systems; Optimierendes Energiemanagement von Brennstoffzelle-Direktspeicher-Hybridsystemen

Energy Technology Data Exchange (ETDEWEB)

Bocklisch, Thilo

2010-03-29

The dissertation presents a new optimizing energy management concept for fuel cell-direct storage-hybrid systems. Initially, the characteristics of specific energy time series are investigated on the basis of real measurement data. A new concept for the multi-scale analysis, modelling and prediction of fluctuating photovoltaic supply and electric load demand profiles is developed. The second part of the dissertation starts with a discussion of the benefits of and the basic coupling and control principles for fuel cell-direct storage-hybrid systems. The typical characteristics of a PEM-fuel cell, a metal hydride hydrogen storage, a lithium-ion battery and a supercap unit are presented. A new modular DC/DC-converter is described. Results from experimental and theoretical investigations of the individual components and the overall hybrid system are discussed. New practicable models for the voltage-current-curve, the state of charge behaviour and the conversion losses are presented. The third part of the dissertation explains the new energy management concept. The optimization of power flows is achieved by a control-oriented approach, employing a) the primary control of bus voltage and fuel cell current, b) the secondary control to limit fuel cell current gradient and operating range and to perform direct storage charge control, and c) the system control to optimally adjust secondary control parameters aiming for a reduction of dynamic fuel cell stress and hydrogen consumption. Results from simulations and experimental investigations demonstrate the benefits and high capabilities of the new optimizing energy management concept. Examples of stationary and portable applications conclude the dissertation. (orig.)

Optimal energy management for a flywheel-based hybrid vehicle

NARCIS (Netherlands)

Berkel, van K.; Hofman, T.; Vroemen, B.G.; Steinbuch, M.

2011-01-01

This paper presents the modeling and design of an optimal Energy Management Strategy (EMS) for a flywheel-based hybrid vehicle, that does not use any electrical motor/generator, or a battery, for its hybrid functionalities. The hybrid drive train consists of only low-cost components, such as a

A new hybrid optimization algorithm CRO-DE for optimal coordination of overcurrent relays in complex power systems

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Mohamed Zellagui

2017-09-01

Full Text Available The paper presents a new hybrid global optimization algorithm based on Chemical Reaction based Optimization (CRO and Di¤erential evolution (DE algorithm for nonlinear constrained optimization problems. This approach proposed for the optimal coordination and setting relays of directional overcurrent relays in complex power systems. In protection coordination problem, the objective function to be minimized is the sum of the operating time of all main relays. The optimization problem is subject to a number of constraints which are mainly focused on the operation of the backup relay, which should operate if a primary relay fails to respond to the fault near to it, Time Dial Setting (TDS, Plug Setting (PS and the minimum operating time of a relay. The hybrid global proposed optimization algorithm aims to minimize the total operating time of each protection relay. Two systems are used as case study to check the effeciency of the optimization algorithm which are IEEE 4-bus and IEEE 6-bus models. Results are obtained and presented for CRO and DE and hybrid CRO-DE algorithms. The obtained results for the studied cases are compared with those results obtained when using other optimization algorithms which are Teaching Learning-Based Optimization (TLBO, Chaotic Differential Evolution Algorithm (CDEA and Modiffied Differential Evolution Algorithm (MDEA, and Hybrid optimization algorithms (PSO-DE, IA-PSO, and BFOA-PSO. From analysing the obtained results, it has been concluded that hybrid CRO-DO algorithm provides the most optimum solution with the best convergence rate.

Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization

International Nuclear Information System (INIS)

Chen, Syuan-Yi; Hung, Yi-Hsuan; Wu, Chien-Hsun; Huang, Siang-Ting

2015-01-01

Highlights: • Online sub-optimal energy management using IPSO. • A second-order HEV model with 5 major segments was built. • IPSO with equivalent-fuel fitness function using 5 particles. • Engine, rule-based control, PSO, IPSO and ECMS are compared. • Max. 31+% fuel economy and 56+% energy consumption improved. - Abstract: This study developed an online suboptimal energy management system by using improved particle swarm optimization (IPSO) for engine/motor hybrid electric vehicles. The vehicle was modeled on the basis of second-order dynamics, and featured five major segments: a battery, a spark ignition engine, a lithium battery, transmission and vehicle dynamics, and a driver model. To manage the power distribution of dual power sources, the IPSO was equipped with three inputs (rotational speed, battery state-of-charge, and demanded torque) and one output (power split ratio). Five steps were developed for IPSO: (1) initialization; (2) determination of the fitness function; (3) selection and memorization; (4) modification of position and velocity; and (5) a stopping rule. Equivalent fuel consumption by the engine and motor was used as the fitness function with five particles, and the IPSO-based vehicle control unit was completed and integrated with the vehicle simulator. To quantify the energy improvement of IPSO, a four-mode rule-based control (system ready, motor only, engine only, and hybrid modes) was designed according to the engine efficiency and rotational speed. A three-loop Equivalent Consumption Minimization Strategy (ECMS) was coded as the best case. The simulation results revealed that IPSO searches the optimal solution more efficiently than conventional PSO does. In two standard driving cycles, ECE and FTP, the improvements in the equivalent fuel consumption and energy consumption compared to baseline were (24.25%, 45.27%) and (31.85%, 56.41%), respectively, for the IPSO. The CO_2 emission for all five cases (pure engine, rule-based, PSO

Solving Bi-Objective Optimal Power Flow using Hybrid method of Biogeography-Based Optimization and Differential Evolution Algorithm: A case study of the Algerian Electrical Network

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Ouafa Herbadji

2016-03-01

Full Text Available This paper proposes a new hybrid metaheuristique algorithm based on the hybridization of Biogeography-based optimization with the Differential Evolution for solving the optimal power flow problem with emission control. The biogeography-based optimization (BBO algorithm is strongly influenced by equilibrium theory of island biogeography, mainly through two steps: Migration and Mutation. Differential Evolution (DE is one of the best Evolutionary Algorithms for global optimization. The hybridization of these two methods is used to overcome traps of local optimal solutions and problems of time consumption. The objective of this paper is to minimize the total fuel cost of generation, total emission, total real power loss and also maintain an acceptable system performance in terms of limits on generator real power, bus voltages and power flow of transmission lines. In the present work, BBO/DE has been applied to solve the optimal power flow problems on IEEE 30-bus test system and the Algerian electrical network 114 bus. The results obtained from this method show better performances compared with DE, BBO and other well known metaheuristique and evolutionary optimization methods.

Optimization of Renewable Energy Hybrid System for Grid Connected Application

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Mustaqimah Mustaqimah

2012-10-01

Full Text Available ABSTRACT. Hybrid energy systems are pollution free, takes low cost and less gestation period, user and social friendly. Such systems are important sources of energy for shops, schools, and clinics in village communities especially in remote areas. Hybrid systems can provide electricity at a comparatively economic price in many remote areas. This paper presents a method to jointly determine the sizing and operation control of hybrid energy systems. The model, PV wind hydro and biomass hybrid system connects to grid. The system configuration of the hybrid is derived based on a theoretical domestic load at a typical location and local solar radiation, wind and water flow rate data and biomass availability. The hybrid energy system is proposed for 10 of teacher’s houses of Industrial Training Institute, Mersing. It is predicted 10 kW load consumption per house. The hybrid energy system consists of wind, solar, biomass, hydro, and grid power. Approximately energy consumption is 860 kWh/day with a 105 kW peak demand load. The proposed hybrid renewable consists of solar photovoltaic (PV panels, wind turbine, hydro turbine and biomass. Battery and inverter are included as part of back-up and storage system. It provides the economic sensitivity of hybridization and the economic and environmental benefits of using a blend of technologies. It also presents the trade off that is involved in optimizing a hybrid energy system to harness and utilize the available renewable energy resources efficiently.

Resizing Technique-Based Hybrid Genetic Algorithm for Optimal Drift Design of Multistory Steel Frame Buildings

Directory of Open Access Journals (Sweden)

Hyo Seon Park

2014-01-01

Full Text Available Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.

Optimized efficiency of all-electric ships by dc hybrid power systems

Science.gov (United States)

Zahedi, Bijan; Norum, Lars E.; Ludvigsen, Kristine B.

2014-06-01

Hybrid power systems with dc distribution are being considered for commercial marine vessels to comply with new stringent environmental regulations, and to achieve higher fuel economy. In this paper, detailed efficiency analysis of a shipboard dc hybrid power system is carried out. An optimization algorithm is proposed to minimize fuel consumption under various loading conditions. The studied system includes diesel engines, synchronous generator-rectifier units, a full-bridge bidirectional converter, and a Li-Ion battery bank as energy storage. In order to evaluate potential fuel saving provided by such a system, an online optimization strategy for fuel consumption is implemented. An Offshore Support Vessel (OSV) is simulated over different operating modes using the online control strategy. The resulted consumed fuel in the simulation is compared to that of a conventional ac power system, and also a dc power system without energy storage. The results show that while the dc system without energy storage provides noticeable fuel saving compared to the conventional ac system, optimal utilization of the energy storage in the dc system results in twice as much fuel saving.

Optimizing Thermal-Elastic Properties of C/C–SiC Composites Using a Hybrid Approach and PSO Algorithm

Science.gov (United States)

Xu, Yingjie; Gao, Tian

2016-01-01

Carbon fiber-reinforced multi-layered pyrocarbon–silicon carbide matrix (C/C–SiC) composites are widely used in aerospace structures. The complicated spatial architecture and material heterogeneity of C/C–SiC composites constitute the challenge for tailoring their properties. Thus, discovering the intrinsic relations between the properties and the microstructures and sequentially optimizing the microstructures to obtain composites with the best performances becomes the key for practical applications. The objective of this work is to optimize the thermal-elastic properties of unidirectional C/C–SiC composites by controlling the multi-layered matrix thicknesses. A hybrid approach based on micromechanical modeling and back propagation (BP) neural network is proposed to predict the thermal-elastic properties of composites. Then, a particle swarm optimization (PSO) algorithm is interfaced with this hybrid model to achieve the optimal design for minimizing the coefficient of thermal expansion (CTE) of composites with the constraint of elastic modulus. Numerical examples demonstrate the effectiveness of the proposed hybrid model and optimization method. PMID:28773343

Development and optimization of methotrexate-loaded lipid-polymer hybrid nanoparticles for controlled drug delivery applications.

Science.gov (United States)

Tahir, Nayab; Madni, Asadullah; Balasubramanian, Vimalkumar; Rehman, Mubashar; Correia, Alexandra; Kashif, Prince Muhammad; Mäkilä, Ermei; Salonen, Jarno; Santos, Hélder A

2017-11-25

Lipid-polymer hybrid nanoparticles (LPHNPs) are emerging platforms for drug delivery applications. In the present study, methotrexate loaded LPHNPs consisted of PLGA and Lipoid S100 were fabricated by employing a single-step modified nanoprecipitation method combined with self-assembly. A three factor, three level Box Behnken design using Design-Expert ® software was employed to access the influence of three independent variables on the particle size, drug entrapment and percent drug release. The optimized formulation was selected through numeric optimization approach. The results were supported with the ANOVA analysis, regression equations and response surface plots. Transmission electron microscope images indicated the nanosized and spherical shape of the LPHNPs with fair size distribution. The nanoparticles ranged from 176 to 308nm, which increased with increased polymer concentration. The increase in polymer and lipid concentration also increased the drug entrapment efficiency. The in vitro drug release was in range 70.34-91.95% and the release mechanism follow the Higuchi model (R 2 =0.9888) and Fickian diffusion (n<0.5). The in vitro cytotoxicity assay and confocal microscopy of the optimized formulation demonstrate the good safety and better internalization of the LPHNPs. The cell antiproliferation showed the spatial and controlled action of the nanoformulation as compared to the plain drug solution. The results suggest that LPHNPs can be a promising delivery system envisioned to safe, stable and potentially controlled delivery of methotrexate to the cancer cells to achieve better therapeutic outcomes. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling and optimization of batteryless hybrid PV (photovoltaic)/Diesel systems for off-grid applications

International Nuclear Information System (INIS)

Tsuanyo, David; Azoumah, Yao; Aussel, Didier; Neveu, Pierre

2015-01-01

This paper presents a new model and optimization procedure for off-grid hybrid PV (photovoltaic)/Diesel systems operating without battery storage. The proposed technico-economic model takes into account the variability of both the solar irradiation and the electrical loads. It allows optimizing the design and the operation of the hybrid systems by searching their lowest LCOE (Levelized Cost of Electricity). Two cases have been investigated: identical Diesel generators and Diesel generators with different sizes, and both are compared to conventional standalone Diesel generator systems. For the same load profile, the optimization results show that the LCOE of the optimized batteryless hybrid solar PV/Diesel (0.289 €/kWh for the hybrid system with identical Diesel generators and 0.284 €/kWh for the hybrid system with different sizes of Diesel generators) is lower than the LCOE obtained with standalone Diesel generators (0.32 €/kWh for the both cases). The obtained results are then confirmed by HOMER (Hybrid Optimization Model for Electric Renewables) software. - Highlights: • A technico-economic model for optimal design and operation management of batteryless hybrid systems is developed. • The model allows optimizing design and operation of hybrid systems by ensuring their lowest LCOE. • The model was validated by HOMER. • Batteryless hybrid system are suitable for off-grid applications

Verification and Optimization of a PLC Control Schedule

NARCIS (Netherlands)

Brinksma, Hendrik; Mader, Angelika H.; Havelund, K.; Penix, J.; Visser, W.

We report on the use of the SPIN model checker for both the verification of a process control program and the derivation of optimal control schedules. This work was carried out as part of a case study for the EC VHS project (Verification of Hybrid Systems), in which the program for a Programmable

Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses

International Nuclear Information System (INIS)

Li, Liang; You, Sixiong; Yang, Chao; Yan, Bingjie; Song, Jian; Chen, Zheng

2016-01-01

Highlights: • The novel approximated global optimal energy management strategy has been proposed for hybrid powertrains. • Eight typical driving behaviors have been classified with K-means to deal with the multiplicative traffic conditions. • The stochastic driver models of different driving behaviors were established based on the Markov chains. • ECMS was used to modify the SMPC-based energy management strategy to improve its fuel economy. • The approximated global optimal energy management strategy for plug-in hybrid electric buses has been verified and analyzed. - Abstract: Driving cycles of a city bus is statistically characterized by some repetitive features, which makes the predictive energy management strategy very desirable to obtain approximate optimal fuel economy of a plug-in hybrid electric bus. But dealing with the complicated traffic conditions and finding an approximated global optimal strategy which is applicable to the plug-in hybrid electric bus still remains a challenging technique. To solve this problem, a novel driving-behavior-aware modified stochastic model predictive control method is proposed for the plug-in hybrid electric bus. Firstly, the K-means is employed to classify driving behaviors, and the driver models based on Markov chains is obtained under different kinds of driving behaviors. While the obtained driver behaviors are regarded as stochastic disturbance inputs, the local minimum fuel consumption might be obtained with a traditional stochastic model predictive control at each step, taking tracking the reference battery state of charge trajectory into consideration in the finite predictive horizons. However, this technique is still accompanied by some working points with reduced/worsened fuel economy. Thus, the stochastic model predictive control is modified with the equivalent consumption minimization strategy to eliminate these undesirable working points. The results in real-world city bus routines show that the

Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block.

Science.gov (United States)

Kora, Padmavathi; Kalva, Sri Ramakrishna

2015-01-01

Abnormal cardiac beat identification is a key process in the detection of heart diseases. Our present study describes a procedure for the detection of left and right bundle branch block (LBBB and RBBB) Electrocardiogram (ECG) patterns. The electrical impulses that control the cardiac beat face difficulty in moving inside the heart. This problem is termed as bundle branch block (BBB). BBB makes it harder for the heart to pump blood effectively through the heart circulatory system. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining two heuristic optimization methods: Bacterial Forging Optimization (BFO) and Particle Swarm Optimization (PSO) for the feature selection of ECG signals. One of the major controlling forces of BFO algorithm is the chemotactic movement of a bacterium that models a test solution. The chemotaxis process of the BFO depends on random search directions which may lead to a delay in achieving the global optimum solution. The hybrid technique: Bacterial Forging-Particle Swarm Optimization (BFPSO) incorporates the concepts from BFO and PSO and it creates individuals in a new generation. This BFPSO method performs local search through the chemotactic movement of BFO and the global search over the entire search domain is accomplished by a PSO operator. The BFPSO feature values are given as the input for the Levenberg-Marquardt Neural Network classifier.

Thermal resistance analysis and optimization of photovoltaic-thermoelectric hybrid system

International Nuclear Information System (INIS)

Yin, Ershuai; Li, Qiang; Xuan, Yimin

2017-01-01

Highlights: • A detailed thermal resistance analysis of the PV-TE hybrid system is proposed. • c-Si PV and p-Si PV cells are proved to be inapplicable for the PV-TE hybrid system. • Some criteria for selecting coupling devices and optimal design are obtained. • A detailed process of designing the practical PV-TE hybrid system is provided. - Abstract: The thermal resistance theory is introduced into the theoretical model of the photovoltaic-thermoelectric (PV-TE) hybrid system. A detailed thermal resistance analysis is proposed to optimize the design of the coupled system in terms of optimal total conversion efficiency. Systems using four types of photovoltaic cells are investigated, including monocrystalline silicon photovoltaic cell, polycrystalline silicon photovoltaic cell, amorphous silicon photovoltaic cell and polymer photovoltaic cell. Three cooling methods, including natural cooling, forced air cooling and water cooling, are compared, which demonstrates a significant superiority of water cooling for the concentrating photovoltaic-thermoelectric hybrid system. Influences of the optical concentrating ratio and velocity of water are studied together and the optimal values are revealed. The impacts of the thermal resistances of the contact surface, TE generator and the upper heat loss thermal resistance on the property of the coupled system are investigated, respectively. The results indicate that amorphous silicon PV cell and polymer PV cell are more appropriate for the concentrating hybrid system. Enlarging the thermal resistance of the thermoelectric generator can significantly increase the performance of the coupled system using amorphous silicon PV cell or polymer PV cell.

Hybrid computer optimization of systems with random parameters

Science.gov (United States)

White, R. C., Jr.

1972-01-01

A hybrid computer Monte Carlo technique for the simulation and optimization of systems with random parameters is presented. The method is applied to the simultaneous optimization of the means and variances of two parameters in the radar-homing missile problem treated by McGhee and Levine.

Numerical optimization of actuator trajectories for ITER hybrid scenario profile evolution

International Nuclear Information System (INIS)

Dongen, J van; Hogeweij, G M D; Felici, F; Geelen, P; Maljaars, E

2014-01-01

Optimal actuator trajectories for an ITER hybrid scenario ramp-up are computed using a numerical optimization method. For both L-mode and H-mode scenarios, the time trajectory of plasma current, EC heating and current drive distribution is determined that minimizes a chosen cost function, while satisfying constraints. The cost function is formulated to reflect two desired properties of the plasma q profile at the end of the ramp-up. The first objective is to maximize the ITG turbulence threshold by maximizing the volume-averaged s/q ratio. The second objective is to achieve a stationary q profile by having a flat loop voltage profile. Actuator and physics-derived constraints are included, imposing limits on plasma current, ramp rates, internal inductance and q profile. This numerical method uses the fast control-oriented plasma profile evolution code RAPTOR, which is successfully benchmarked against more complete CRONOS simulations for L-mode and H-mode mode ITER hybrid scenarios. It is shown that the optimized trajectories computed using RAPTOR also result in an improved ramp-up scenario for CRONOS simulations using the same input trajectories. Furthermore, the optimal trajectories are shown to vary depending on the precise timing of the L–H transition. (paper)

Power and mass optimization of the hybrid solar panel and thermoelectric generators

International Nuclear Information System (INIS)

Kwan, Trevor Hocksun; Wu, Xiaofeng

2016-01-01

Highlights: • The dynamics of the hybrid PV/TEG system operating in outer space is studied. • A generalized thermodynamic model of the hybrid PV/TEG system is given. • This model is then simplified to consider the outer space scenario. • The design of the hybrid PV/TEG system is optimized using the NSGA-II algorithm. • The optimized hybrid system is more efficient than its monolithic counterparts. - Abstract: The thermoelectric generator (TEG) has been widely considered as an electrical power source in many ground applications because of its clean and noiseless characteristics. Moreover, the hybrid photovoltaic cell and TEG (PV/TEG) system has also received wide attention due to its improved power conversion efficiency over its monolithic counterparts. This paper presents a study of the dynamics and the operation of the hybrid PV/TEG system in an outer space environment where a unified thermodynamic model of this system is presented. Moreover, the multi-objective NSGA-II genetic algorithm is utilized to optimize the design of the TEG both in terms of optimal output power and in terms of mass. Specifically, the design of the single stage and the two stage variant of the aforementioned TEG are considered. Simulation results indicate that the optimized PV/TEG system does indeed achieve better efficiencies than that of the monolithic counterparts. Furthermore, it is shown that the single stage TEG is more beneficial than the two stage TEG in terms of achieving optimal performance.

Hybrid Control System for Greater Resilience Using Multiple Isolation and Building Connection

Directory of Open Access Journals (Sweden)

Masaki Taniguchi

2016-10-01

Full Text Available An innovative hybrid control building system of multiple isolation and connection is proposed and investigated using both time-history and input energy responses for various types of ground motions together with transfer functions. It is concerned that the seismic displacement response at the base-isolation layer of the existing base-isolated buildings may extremely increase under long-period and long-duration ground motions which are getting great attention recently. In order to enhance the seismic performance of those base-isolated buildings, a novel hybrid system of multiple isolation and building-connection is proposed and compared with other structural systems such as an independent multiple isolation system, a hybrid system of base-isolation and building-connection. Furthermore, the robustness of seismic responses of the proposed hybrid system for various types of ground motion is discussed through the comparison of various structural systems including non-hybrid systems. Finally the optimal connection damper location is investigated using a sensitivity-type optimization approach.

Hybrid Design Optimization of High Voltage Pulse Transformers for Klystron Modulators

CERN Document Server

Sylvain, Candolfi; Davide, Aguglia; Jerome, Cros

2015-01-01

This paper presents a hybrid optimization methodology for the design of high voltage pulse transformers used in klystron modulators. The optimization process is using simplified 2D FEA design models of the 3D transformer structure. Each intermediate optimal solution is evaluated by 3D FEA and correction coefficients of the 2D FEA models are derived. A new optimization process using 2D FEA models is then performed. The convergence of this hybrid optimal design methodology is obtained with a limited number of time consuming 3D FEA simulations. The method is applied to the optimal design of a monolithic high voltage pulse transformer for the CLIC klystron modulator.

Model predictive control-based efficient energy recovery control strategy for regenerative braking system of hybrid electric bus

International Nuclear Information System (INIS)

Li, Liang; Zhang, Yuanbo; Yang, Chao; Yan, Bingjie; Marina Martinez, C.

2016-01-01

Highlights: • A 7-degree-of-freedom model of hybrid electric vehicle with regenerative braking system is built. • A modified nonlinear model predictive control strategy is developed. • The particle swarm optimization algorithm is employed to solve the optimization problem. • The proposed control strategy is verified by simulation and hardware-in-loop tests. • Test results verify the effectiveness of the proposed control strategy. - Abstract: As one of the main working modes, the energy recovered with regenerative braking system provides an effective approach so as to greatly improve fuel economy of hybrid electric bus. However, it is still a challenging issue to ensure braking stability while maximizing braking energy recovery. To solve this problem, an efficient energy recovery control strategy is proposed based on the modified nonlinear model predictive control method. Firstly, combined with the characteristics of the compound braking process of single-shaft parallel hybrid electric bus, a 7 degrees of freedom model of the vehicle longitudinal dynamics is built. Secondly, considering nonlinear characteristic of the vehicle model and the efficiency of regenerative braking system, the particle swarm optimization algorithm within the modified nonlinear model predictive control is adopted to optimize the torque distribution between regenerative braking system and pneumatic braking system at the wheels. So as to reduce the computational time of modified nonlinear model predictive control, a nearest point method is employed during the braking process. Finally, the simulation and hardware-in-loop test are carried out on road conditions with different tire–road adhesion coefficients, and the proposed control strategy is verified by comparing it with the conventional control method employed in the baseline vehicle controller. The simulation and hardware-in-loop test results show that the proposed strategy can ensure vehicle safety during emergency braking

A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system

International Nuclear Information System (INIS)

Kim, Kihyung; Spakovsky, Michael R. von; Wang, M.; Nelson, Douglas J.

2011-01-01

During system development, large-scale, complex energy systems require multi-disciplinary efforts to achieve system quality, cost, and performance goals. As systems become larger and more complex, the number of possible system configurations and technologies, which meet the designer's objectives optimally, increases greatly. In addition, both transient and environmental effects may need to be taken into account. Thus, the difficulty of developing the system via the formulation of a single optimization problem in which the optimal synthesis/design and operation/control of the system are achieved simultaneously is great and rather problematic. This difficulty is further heightened with the introduction of uncertainty analysis, which transforms the problem from a purely deterministic one into a probabilistic one. Uncertainties, system complexity and nonlinearity, and large numbers of decision variables quickly render the single optimization problem unsolvable by conventional, single-level, optimization strategies. To address these difficulties, the strategy adopted here combines a dynamic physical decomposition technique for large-scale optimization with a response sensitivity analysis method for quantifying system response uncertainties to given uncertainty sources. The feasibility of such a hybrid approach is established by applying it to the synthesis/design and operation/control of a 5 kW proton exchange membrane (PEM) fuel cell system.

Controllable chaos in hybrid electro-optomechanical systems

Science.gov (United States)

Wang, Mei; Lü, Xin-You; Ma, Jin-Yong; Xiong, Hao; Si, Liu-Gang; Wu, Ying

2016-01-01

We investigate the nonlinear dynamics of a hybrid electro-optomechanical system (EOMS) that allows us to realize the controllable opto-mechanical nonlinearity by driving the microwave LC resonator with a tunable electric field. A controllable optical chaos is realized even without changing the optical pumping. The threshold and lifetime of the chaos could be optimized by adjusting the strength, frequency, or phase of the electric field. This study provides a method of manipulating optical chaos with an electric field. It may offer the prospect of exploring the controllable chaos in on-chip optoelectronic devices and its applications in secret communication. PMID:26948505

Controllable chaos in hybrid electro-optomechanical systems.

Science.gov (United States)

Wang, Mei; Lü, Xin-You; Ma, Jin-Yong; Xiong, Hao; Si, Liu-Gang; Wu, Ying

2016-03-07

We investigate the nonlinear dynamics of a hybrid electro-optomechanical system (EOMS) that allows us to realize the controllable opto-mechanical nonlinearity by driving the microwave LC resonator with a tunable electric field. A controllable optical chaos is realized even without changing the optical pumping. The threshold and lifetime of the chaos could be optimized by adjusting the strength, frequency, or phase of the electric field. This study provides a method of manipulating optical chaos with an electric field. It may offer the prospect of exploring the controllable chaos in on-chip optoelectronic devices and its applications in secret communication.

Genetic algorithm based optimization on modeling and design of hybrid renewable energy systems

International Nuclear Information System (INIS)

Ismail, M.S.; Moghavvemi, M.; Mahlia, T.M.I.

2014-01-01

Highlights: • Solar data was analyzed in the location under consideration. • A program was developed to simulate operation of the PV hybrid system. • Genetic algorithm was used to optimize the sizes of the hybrid system components. • The costs of the pollutant emissions were considered in the optimization. • It is cost effective to power houses in remote areas with such hybrid systems. - Abstract: A sizing optimization of a hybrid system consisting of photovoltaic (PV) panels, a backup source (microturbine or diesel), and a battery system minimizes the cost of energy production (COE), and a complete design of this optimized system supplying a small community with power in the Palestinian Territories is presented in this paper. A scenario that depends on a standalone PV, and another one that depends on a backup source alone were analyzed in this study. The optimization was achieved via the usage of genetic algorithm. The objective function minimizes the COE while covering the load demand with a specified value for the loss of load probability (LLP). The global warming emissions costs have been taken into account in this optimization analysis. Solar radiation data is firstly analyzed, and the tilt angle of the PV panels is then optimized. It was discovered that powering a small rural community using this hybrid system is cost-effective and extremely beneficial when compared to extending the utility grid to supply these remote areas, or just using conventional sources for this purpose. This hybrid system decreases both operating costs and the emission of pollutants. The hybrid system that realized these optimization purposes is the one constructed from a combination of these sources

Real-Time Energy Management Control for Hybrid Electric Powertrains

Directory of Open Access Journals (Sweden)

Mohamed Zaher

2013-01-01

Full Text Available This paper focuses on embedded control of a hybrid powertrain concepts for mobile vehicle applications. Optimal robust control approach is used to develop a real-time energy management strategy. The main idea is to store the normally wasted mechanical regenerative energy in energy storage devices for later usage. The regenerative energy recovery opportunity exists in any condition where the speed of motion is in the opposite direction to the applied force or torque. This is the case when the vehicle is braking, decelerating, the motion is driven by gravitational force, or load driven. There are three main concepts for energy storing devices in hybrid vehicles: electric, hydraulic, and mechanical (flywheel. The real-time control challenge is to balance the system power demands from the engine and the hybrid storage device, without depleting the energy storage device or stalling the engine in any work cycle. In the worst-case scenario, only the engine is used and the hybrid system is completely disabled. A rule-based control algorithm is developed and is tuned for different work cycles and could be linked to a gain scheduling algorithm. A gain scheduling algorithm identifies the cycle being performed by the work machine and its position via GPS and maps both of them to the gains.

Optimizations of spin-exchange relaxation-free magnetometer based on potassium and rubidium hybrid optical pumping

International Nuclear Information System (INIS)

Fang, Jiancheng; Wang, Tao; Li, Yang; Zhang, Hong; Zou, Sheng

2014-01-01

The hybrid optical pumping atomic magnetometers have not realized its theoretical sensitivity, the optimization is critical for optimal performance. The optimizations proposed in this paper are suitable for hybrid optical pumping atomic magnetometer, which contains two alkali species. To optimize the parameters, the dynamic equations of spin evolution with two alkali species were solved, whose steady-state solution is used to optimize the parameters. The demand of the power of the pump beam is large for hybrid optical pumping. Moreover, the sensitivity of the hybrid optical pumping magnetometer increases with the increase of the power density of the pump beam. The density ratio between the two alkali species is especially important for hybrid optical pumping magnetometer. A simple expression for optimizing the density ratio is proposed in this paper, which can help to determine the mole faction of the alkali atoms in fabricating the hybrid cell before the cell is sealed. The spin-exchange rate between the two alkali species is proportional to the saturated density of the alkali vapor, which is highly dependent on the temperature of the cell. Consequently, the sensitivity of the hybrid optical pumping magnetometer is dependent on the temperature of the cell. We proposed the thermal optimization of the hybrid cell for a hybrid optical pumping magnetometer, which can improve the sensitivity especially when the power of the pump beam is low. With these optimizations, a sensitivity of approximately 5 fT/Hz 1/2 is achieved with gradiometer arrangement

The BLAIRR Irradiation Facility Hybrid Spallation Target Optimization

Energy Technology Data Exchange (ETDEWEB)

Simos N.; Hanson A.; Brown, D.; Elbakhshawn, M.

2016-04-11

BLAIRR STUDY STATUS OVERVIEW Beamline Complex Evaluation/Assessment and Adaptation to the Goals Facility Radiological Constraints ? Large scale analyses of conventional facility and integrated shield (concrete, soil)Target Optimization and Design: Beam-target interaction optimization Hadronic interaction and energy deposition limitations Single phase and Hybrid target concepts Irradiation Damage Thermo-mechanical considerations Spallation neutron fluence optimization for (a) fast neutron irradiation damage (b) moderator/reflector studies, (c) NTOF potential and optimization (d) mono-energetic neutron beam

Toward a Smart Car: Hybrid Nonlinear Predictive Controller With Adaptive Horizon

Czech Academy of Sciences Publication Activity Database

Pčolka, M.; Žáčeková, E.; Čelikovský, Sergej; Šebek, M.

(2018), č. článku 08059760. ISSN 1063-6536 R&D Projects: GA ČR(CZ) GA17-04682S Institutional support: RVO:67985556 Keywords : Autonomous vehicles * hybrid systems * nonlinear model predictive control (MPC) * optimization * vehicle control Subject RIV: BC - Control Systems Theory Impact factor: 3.882, year: 2016 http://ieeexplore.ieee.org/document/8059760/

A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

Directory of Open Access Journals (Sweden)

Zhenzhen Lei

2017-01-01

Full Text Available The driving pattern has an important influence on the parameter optimization of the energy management strategy (EMS for hybrid electric vehicles (HEVs. A new algorithm using simulated annealing particle swarm optimization (SA-PSO is proposed for parameter optimization of both the power system and control strategy of HEVs based on multiple driving cycles in order to realize the minimum fuel consumption without impairing the dynamic performance. Furthermore, taking the unknown of the actual driving cycle into consideration, an optimization method of the dynamic EMS based on driving pattern recognition is proposed in this paper. The simulation verifications for the optimized EMS based on multiple driving cycles and driving pattern recognition are carried out using Matlab/Simulink platform. The results show that compared with the original EMS, the former strategy reduces the fuel consumption by 4.36% and the latter one reduces the fuel consumption by 11.68%. A road test on the prototype vehicle is conducted and the effectiveness of the proposed EMS is validated by the test data.

Operation management of daily economic dispatch using novel hybrid particle swarm optimization and gravitational search algorithm with hybrid mutation strategy

Science.gov (United States)

Wang, Yan; Huang, Song; Ji, Zhicheng

2017-07-01

This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.

SFC Optimization for Aero Engine Based on Hybrid GA-SQP Method

Science.gov (United States)

Li, Jie; Fan, Ding; Sreeram, Victor

2013-12-01

This study focuses on on-line specific fuel consumption (SFC) optimization of aero engines. For solving this optimization problem, a nonlinear pneumatic and thermodynamics model of the aero engine is built and a hybrid optimization technique which is formed by combining the genetic algorithm (GA) and the sequential quadratic programming (SQP) is presented. The ability of standard GA and standard SQP in solving this type of problem is investigated. It has been found that, although the SQP is fast, very little SFC reductions can be obtained. The GA is able to solve the problem well but a lot of computational time is needed. The presented hybrid GA-SQP gives a good SFC optimization effect and saves 76.6% computational time when compared to the standard GA. It has been shown that the hybrid GA-SQP is a more effective and higher real-time method for SFC on-line optimization of the aero engine.

Feasibility Study and Optimization of An Hybrid System (Eolian ...

African Journals Online (AJOL)

Feasibility Study and Optimization of An Hybrid System (Eolian- Photovoltaic - Diesel) With Provision of Electric Energy Completely Independent. ... reducing emissions of greenhouse gas (CO2 rate = 16086 kg / year for a system using only the generator diesel and is 599 kg / year for the stand alone hybrid system studied).

The control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle using a CMAC neural network.

Science.gov (United States)

Harmon, Frederick G; Frank, Andrew A; Joshi, Sanjay S

2005-01-01

A Simulink model, a propulsion energy optimization algorithm, and a CMAC controller were developed for a small parallel hybrid-electric unmanned aerial vehicle (UAV). The hybrid-electric UAV is intended for military, homeland security, and disaster-monitoring missions involving intelligence, surveillance, and reconnaissance (ISR). The Simulink model is a forward-facing simulation program used to test different control strategies. The flexible energy optimization algorithm for the propulsion system allows relative importance to be assigned between the use of gasoline, electricity, and recharging. A cerebellar model arithmetic computer (CMAC) neural network approximates the energy optimization results and is used to control the parallel hybrid-electric propulsion system. The hybrid-electric UAV with the CMAC controller uses 67.3% less energy than a two-stroke gasoline-powered UAV during a 1-h ISR mission and 37.8% less energy during a longer 3-h ISR mission.

Optimization-Based Controllers for Robotics Applications (OCRA: The Case of iCub’s Whole-Body Control

Directory of Open Access Journals (Sweden)

Jorhabib G. Eljaik

2018-03-01

Full Text Available OCRA stands for Optimization-based Control for Robotics Applications. It consists of a set of platform-independent libraries which facilitates the development of optimization-based controllers for articulated robots. Hierarchical, weighted, and hybrid control strategies can easily be implemented using these tools. The generic interfaces provided by OCRA allow different robots to use the exact same controllers. OCRA also allows users to specify high-level objectives via tasks. These tasks provide an intuitive way of generating complex behaviors and can be specified in XML format. To illustrate the use of OCRA, an implementation of interest to this research topic for the humanoid robot iCub is presented. OCRA stands for Optimization-based Control for Robotics Applications. It consists of a set of platform-independent libraries which facilitates the development of optimization-based controllers for articulated robots. Hierarchical, weighted, and hybrid control strategies can easily be implemented using these tools. The generic interfaces provided by OCRA allow different robots to use the exact same controllers. OCRA also allows users to specify high-level objectives via tasks. These tasks provide an intuitive way of generating complex behaviors and can be specified in XML format. To illustrate the use of OCRA, an implementation of interest to this research topic for the humanoid robot iCub is presented.

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

Directory of Open Access Journals (Sweden)

Jianwen Guo

2016-01-01

Full Text Available All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO and cuckoo search (CS algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

Directory of Open Access Journals (Sweden)

Qingyang Zhang

2015-02-01

Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

Optimization of hybrid model on hajj travel

Science.gov (United States)

Cahyandari, R.; Ariany, R. L.; Sukono

2018-03-01

Hajj travel insurance is an insurance product offered by the insurance company in preparing funds to perform the pilgrimage. This insurance product helps would-be pilgrims to set aside a fund of saving hajj with regularly, but also provides funds of profit sharing (mudharabah) and insurance protection. Scheme of insurance product fund management is largely using the hybrid model, which is the fund from would-be pilgrims will be divided into three account management, that is personal account, tabarru’, and ujrah. Scheme of hybrid model on hajj travel insurance was already discussed at the earlier paper with titled “The Hybrid Model Algorithm on Sharia Insurance”, taking the example case of Mitra Mabrur Plus product from Bumiputera company. On these advanced paper, will be made the previous optimization model design, with partition of benefit the tabarru’ account. Benefits such as compensation for 40 critical illness which initially only for participants of insurance only, on optimization is intended for participants of the insurance and his heir, also to benefit the hospital bills. Meanwhile, the benefits of death benefit is given if the participant is fixed die.

Modeling and energy management control design for a fuel cell hybrid passenger bus

Science.gov (United States)

Simmons, Kyle; Guezennec, Yann; Onori, Simona

2014-01-01

This paper presents the modeling and supervisory energy management design of a hybrid fuel cell/battery-powered passenger bus. With growing concerns about petroleum usage and greenhouse gas emissions in the transportation sector, finding alternative methods for vehicle propulsion is necessary. Proton Exchange Membrane (PEM) fuel cell systems are viable possibilities for energy converters due to their high efficiencies and zero emissions. It has been shown that the benefits of PEM fuel cell systems can be greatly improved through hybridization. In this work, the challenge of developing an on-board energy management strategy with near-optimal performance is addressed by a two-step process. First, an optimal control based on Pontryagin's Minimum Principle (PMP) is implemented to find the global optimal solution which minimizes fuel consumption, for different drive cycles, with and without grade. The optimal solutions are analyzed in order to aid in development of a practical controller suitable for on-board implementation, in the form of an Auto-Regressive Moving Average (ARMA) regulator. Simulation results show that the ARMA controller is capable of achieving fuel economy within 3% of the PMP controller while being able to limit the transient demand on the fuel cell system.

Design and Validation of Real-Time Optimal Control with ECMS to Minimize Energy Consumption for Parallel Hybrid Electric Vehicles

Directory of Open Access Journals (Sweden)

Aiyun Gao

2017-01-01

Full Text Available A real-time optimal control of parallel hybrid electric vehicles (PHEVs with the equivalent consumption minimization strategy (ECMS is presented in this paper, whose purpose is to achieve the total equivalent fuel consumption minimization and to maintain the battery state of charge (SOC within its operation range at all times simultaneously. Vehicle and assembly models of PHEVs are established, which provide the foundation for the following calculations. The ECMS is described in detail, in which an instantaneous cost function including the fuel energy and the electrical energy is proposed, whose emphasis is the computation of the equivalent factor. The real-time optimal control strategy is designed through regarding the minimum of the total equivalent fuel consumption as the control objective and the torque split factor as the control variable. The validation of the control strategy proposed is demonstrated both in the MATLAB/Simulink/Advisor environment and under actual transportation conditions by comparing the fuel economy, the charge sustainability, and parts performance with other three control strategies under different driving cycles including standard, actual, and real-time road conditions. Through numerical simulations and real vehicle tests, the accuracy of the approach used for the evaluation of the equivalent factor is confirmed, and the potential of the proposed control strategy in terms of fuel economy and keeping the deviations of SOC at a low level is illustrated.

Automated hybrid closed-loop control with a proportional-integral-derivative based system in adolescents and adults with type 1 diabetes: individualizing settings for optimal performance.

Science.gov (United States)

Ly, Trang T; Weinzimer, Stuart A; Maahs, David M; Sherr, Jennifer L; Roy, Anirban; Grosman, Benyamin; Cantwell, Martin; Kurtz, Natalie; Carria, Lori; Messer, Laurel; von Eyben, Rie; Buckingham, Bruce A

2017-08-01

Automated insulin delivery systems, utilizing a control algorithm to dose insulin based upon subcutaneous continuous glucose sensor values and insulin pump therapy, will soon be available for commercial use. The objective of this study was to determine the preliminary safety and efficacy of initialization parameters with the Medtronic hybrid closed-loop controller by comparing percentage of time in range, 70-180 mg/dL (3.9-10 mmol/L), mean glucose values, as well as percentage of time above and below target range between sensor-augmented pump therapy and hybrid closed-loop, in adults and adolescents with type 1 diabetes. We studied an initial cohort of 9 adults followed by a second cohort of 15 adolescents, using the Medtronic hybrid closed-loop system with the proportional-integral-derivative with insulin feed-back (PID-IFB) algorithm. Hybrid closed-loop was tested in supervised hotel-based studies over 4-5 days. The overall mean percentage of time in range (70-180 mg/dL, 3.9-10 mmol/L) during hybrid closed-loop was 71.8% in the adult cohort and 69.8% in the adolescent cohort. The overall percentage of time spent under 70 mg/dL (3.9 mmol/L) was 2.0% in the adult cohort and 2.5% in the adolescent cohort. Mean glucose values were 152 mg/dL (8.4 mmol/L) in the adult cohort and 153 mg/dL (8.5 mmol/L) in the adolescent cohort. Closed-loop control using the Medtronic hybrid closed-loop system enables adaptive, real-time basal rate modulation. Initializing hybrid closed-loop in clinical practice will involve individualizing initiation parameters to optimize overall glucose control. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

Science.gov (United States)

Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

2015-02-01

This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

Direct switching control of DC-DC power electronic converters using hybrid system theory

Energy Technology Data Exchange (ETDEWEB)

Zhao, J.; Lin, F. [Wayne State Univ., Detroit, MI (United States). Dept. of Electrical and Computer Engineering; Wang, C. [Wayne State Univ., Detroit, MI (United States). Dept. of Electrical and Computer Engineering; Wayne State Univ., Detroit, MI (United States). Div. of Engineering Technology

2010-07-01

A direct switching control (DSC) scheme for power electronics converters was described. The system was designed for use in both traditional and renewable energy applications as well as in electric drive vehicles. The proposed control scheme was based on a detailed hybrid system converter model that used model predictive control (MPC), piecewise affine (PWA) approximations and constrained optimal control methods. A DC-DC converter was modelled as a hybrid machine. Switching among different modes of the DC-DC converter were modelled as discrete events controlled by the hybrid controller. The modelling scheme was applied to a Buck converter. The DSC was used to control the switch of the power converter based on a hybrid machine model. Results of the study showed that the method can be used to regulate output voltage and inductor currents. The method also provides fast transient responses and effectively regulates both currents and voltage. The controller can be used to provide immediate responses to dynamic disturbances and output voltage fluctuations. 23 refs., 7 figs.

Integrated powertrain control for optimizing CO2-NOx emission trade-off in heavy duty hybrid electric vehicles

NARCIS (Netherlands)

Kessels, J.T.B.A.; Willems, F.P.T.; Spronkmans, S.J.

2011-01-01

Energy management in modern vehicles typically relates to optimizing the powerflow in the (hybrid) powertrain, whereas emission management is associated with the combustion engine and its aftertreatment system. To achieve maximum performance in both fuel economy and hazardous emissions, the concept

A Novel Hybrid Firefly Algorithm for Global Optimization.

Directory of Open Access Journals (Sweden)

Lina Zhang

Full Text Available Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA, is proposed by combining the advantages of both the firefly algorithm (FA and differential evolution (DE. FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA, differential evolution (DE and particle swarm optimization (PSO in the sense of avoiding local minima and increasing the convergence rate.

Optimal reactive power and voltage control in distribution networks with distributed generators by fuzzy adaptive hybrid particle swarm optimisation method

DEFF Research Database (Denmark)

Chen, Shuheng; Hu, Weihao; Su, Chi

2015-01-01

A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid PSO (FAHPSO) is proposed. The objective is to minimize comprehensive cost, consisting of power loss and operation cost of transformers...... that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods....... algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33-node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove...

Optimal integration strategies for a syngas fuelled SOFC and gas turbine hybrid

Science.gov (United States)

Zhao, Yingru; Sadhukhan, Jhuma; Lanzini, Andrea; Brandon, Nigel; Shah, Nilay

This article aims to develop a thermodynamic modelling and optimization framework for a thorough understanding of the optimal integration of fuel cell, gas turbine and other components in an ambient pressure SOFC-GT hybrid power plant. This method is based on the coupling of a syngas-fed SOFC model and an associated irreversible GT model, with an optimization algorithm developed using MATLAB to efficiently explore the range of possible operating conditions. Energy and entropy balance analysis has been carried out for the entire system to observe the irreversibility distribution within the plant and the contribution of different components. Based on the methodology developed, a comprehensive parametric analysis has been performed to explore the optimum system behavior, and predict the sensitivity of system performance to the variations in major design and operating parameters. The current density, operating temperature, fuel utilization and temperature gradient of the fuel cell, as well as the isentropic efficiencies and temperature ratio of the gas turbine cycle, together with three parameters related to the heat transfer between subsystems are all set to be controllable variables. Other factors affecting the hybrid efficiency have been further simulated and analysed. The model developed is able to predict the performance characteristics of a wide range of hybrid systems potentially sizing from 2000 to 2500 W m -2 with efficiencies varying between 50% and 60%. The analysis enables us to identify the system design tradeoffs, and therefore to determine better integration strategies for advanced SOFC-GT systems.

Control strategy of hydraulic/electric synergy system in heavy hybrid vehicles

Energy Technology Data Exchange (ETDEWEB)

Sun Hui; Yang Lifu; Junqing Jing; Yanling Luo [Jiangsu Xuzhou Construction Machinery Research Institute, Jiangsu (China)

2011-01-15

Energy consumption and exhaust emissions of hybrid vehicles strongly depend on the energy storage source and the applied control strategy. Heavy vehicles have the characteristics of frequent starts/stops and significant amounts of braking energy, which needs to find a more efficient way to store and use the high power flow. A novel parallel hybrid vehicles configuration consisting of hydraulic/electric synergy system is proposed to overcome the existing drawbacks of single energy storage source in heavy hybrid vehicles. A control strategy combining a logic threshold approach and key parameters optimization algorithm is developed to achieve acceptable vehicle performance while simultaneously maximizing engine fuel economy and maintaining the battery state of charge in its rational operation range at all times. The experimental and simulation results illustrate the potential of the proposed control strategy in terms of fuel economy and in keeping the deviations of SOC at high efficiency range. (author)

Control strategy of hydraulic/electric synergy system in heavy hybrid vehicles

International Nuclear Information System (INIS)

Sun Hui; Yang Lifu; Jing Junqing; Luo Yanling

2011-01-01

Energy consumption and exhaust emissions of hybrid vehicles strongly depend on the energy storage source and the applied control strategy. Heavy vehicles have the characteristics of frequent starts/stops and significant amounts of braking energy, which needs to find a more efficient way to store and use the high power flow. A novel parallel hybrid vehicles configuration consisting of hydraulic/electric synergy system is proposed to overcome the existing drawbacks of single energy storage source in heavy hybrid vehicles. A control strategy combining a logic threshold approach and key parameters optimization algorithm is developed to achieve acceptable vehicle performance while simultaneously maximizing engine fuel economy and maintaining the battery state of charge in its rational operation range at all times. The experimental and simulation results illustrate the potential of the proposed control strategy in terms of fuel economy and in keeping the deviations of SOC at high efficiency range.

Optimal control of cooperative multi-vehicle systems; Optimalsteuerung kooperierender Mehrfahrzeugsysteme

Energy Technology Data Exchange (ETDEWEB)

Reinl, Christian; Stryk, Oskar von [Technische Univ. Darmstadt (Germany). FB Informatik; Glocker, Markus [Trimble Terrasat GmbH, Hoehenkirchen (Germany)

2009-07-01

Nonlinear hybrid dynamical systems for modeling optimal cooperative control enable a tight and formal coupling of discrete and continuous state dynamics, i.e. of dynamic role and task assignment with switching dynamics of motions. In the resulting mixed-integer multi-phase optimal control problems constraints on the discrete and continuous state and control variables can be considered, e.g., formation or communication requirements. Two numerical methods are investigated: a decomposition approach using branch-and-bound and direct collocation methods as well as an approximation by large-scale, mixed-integer linear problems. Both methods are applied to example problems: the optimal simultaneous waypoint sequencing and trajectory planning of a team of aerial vehicles and the optimization of role distribution and trajectories in robot soccer. (orig.)

Neural network control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle

Science.gov (United States)

Harmon, Frederick G.

2005-11-01

Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles (UAVs) used for military, homeland security, and disaster-monitoring missions. The benefits, due to the hybrid and electric-only modes, include increased time-on-station and greater range as compared to electric-powered UAVs and stealth modes not available with gasoline-powered UAVs. This dissertation contributes to the research fields of small unmanned aerial vehicles, hybrid-electric propulsion system control, and intelligent control. A conceptual design of a small UAV with a parallel hybrid-electric propulsion system is provided. The UAV is intended for intelligence, surveillance, and reconnaissance (ISR) missions. A conceptual design reveals the trade-offs that must be considered to take advantage of the hybrid-electric propulsion system. The resulting hybrid-electric propulsion system is a two-point design that includes an engine primarily sized for cruise speed and an electric motor and battery pack that are primarily sized for a slower endurance speed. The electric motor provides additional power for take-off, climbing, and acceleration and also serves as a generator during charge-sustaining operation or regeneration. The intelligent control of the hybrid-electric propulsion system is based on an instantaneous optimization algorithm that generates a hyper-plane from the nonlinear efficiency maps for the internal combustion engine, electric motor, and lithium-ion battery pack. The hyper-plane incorporates charge-depletion and charge-sustaining strategies. The optimization algorithm is flexible and allows the operator/user to assign relative importance between the use of gasoline, electricity, and recharging depending on the intended mission. A MATLAB/Simulink model was developed to test the control algorithms. The Cerebellar Model Arithmetic Computer (CMAC) associative memory neural network is applied to the control of the UAVs parallel hybrid

Hybrid spacecraft attitude control system

Directory of Open Access Journals (Sweden)

Renuganth Varatharajoo

2016-02-01

Full Text Available The hybrid subsystem design could be an attractive approach for futurespacecraft to cope with their demands. The idea of combining theconventional Attitude Control System and the Electrical Power System ispresented in this article. The Combined Energy and Attitude ControlSystem (CEACS consisting of a double counter rotating flywheel assemblyis investigated for small satellites in this article. Another hybrid systemincorporating the conventional Attitude Control System into the ThermalControl System forming the Combined Attitude and Thermal ControlSystem (CATCS consisting of a "fluid wheel" and permanent magnets isalso investigated for small satellites herein. The governing equationsdescribing both these novel hybrid subsystems are presented and theironboard architectures are numerically tested. Both the investigated novelhybrid spacecraft subsystems comply with the reference missionrequirements.The hybrid subsystem design could be an attractive approach for futurespacecraft to cope with their demands. The idea of combining theconventional Attitude Control System and the Electrical Power System ispresented in this article. The Combined Energy and Attitude ControlSystem (CEACS consisting of a double counter rotating flywheel assemblyis investigated for small satellites in this article. Another hybrid systemincorporating the conventional Attitude Control System into the ThermalControl System forming the Combined Attitude and Thermal ControlSystem (CATCS consisting of a "fluid wheel" and permanent magnets isalso investigated for small satellites herein. The governing equationsdescribing both these novel hybrid subsystems are presented and theironboard architectures are numerically tested. Both the investigated novelhybrid spacecraft subsystems comply with the reference missionrequirements.

Game-based Abstraction and Controller Synthesis for Probabilistic Hybrid Systems

DEFF Research Database (Denmark)

Hahn, Ernst Moritz; Norman, Gethin; Parker, David

2011-01-01

We consider a class of hybrid systems that involve random phenomena, in addition to discrete and continuous behaviour. Examples of such systems include wireless sensing and control applications. We propose and compare two abstraction techniques for this class of models, which yield lower and upper...... bounds on the optimal probability of reaching a particular class of states. We also demonstrate the applicability of these abstraction techniques to the computation of long-run average reward properties and the synthesis of controllers. The first of the two abstractions yields more precise information......, while the second is easier to construct. For the latter, we demonstrate how existing solvers for hybrid systems can be leveraged to perform the computation....

Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes

Directory of Open Access Journals (Sweden)

Jaya Shankar Tumuluru

2016-11-01

Full Text Available Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article demonstrates the novelty of the hybrid genetic algorithm (HGA, which combines both stochastic and deterministic routines for improved optimization results. The new hybrid genetic algorithm developed is applied to the Ackley benchmark function as well as case studies in food, biofuel, and biotechnology processes. For each case study, the hybrid genetic algorithm found a better optimum candidate than reported by the sources. In the case of food processing, the hybrid genetic algorithm improved the anthocyanin yield by 6.44%. Optimization of bio-oil production using HGA resulted in a 5.06% higher yield. In the enzyme production process, HGA predicted a 0.39% higher xylanase yield. Hybridization of the genetic algorithm with a deterministic algorithm resulted in an improved optimum compared to statistical methods.

Operating Point Optimization of a Hydrogen Fueled Hybrid Solid Oxide Fuel Cell-Steam Turbine (SOFC-ST Plant

Directory of Open Access Journals (Sweden)

Juanjo Ugartemendia

2013-09-01

Full Text Available This paper presents a hydrogen powered hybrid solid oxide fuel cell-steam turbine (SOFC-ST system and studies its optimal operating conditions. This type of installation can be very appropriate to complement the intermittent generation of renewable energies, such as wind generation. A dynamic model of an alternative hybrid SOFC-ST configuration that is especially suited to work with hydrogen is developed. The proposed system recuperates the waste heat of the high temperature fuel cell, to feed a bottoming cycle (BC based on a steam turbine (ST. In order to optimize the behavior and performance of the system, a two-level control structure is proposed. Two controllers have been implemented for the stack temperature and fuel utilization factor. An upper supervisor generates optimal set-points in order to reach a maximal hydrogen efficiency. The simulation results obtained show that the proposed system allows one to reach high efficiencies at rated power levels.

Hybrid SPECT/CT: Principle, dosimetry and quality control; Imagerie hybride: principe, dosimetrie et controle de qualite

Energy Technology Data Exchange (ETDEWEB)

Hapdey, S.; Gardin, I.; Salles, A.; Rousseliere, F.; Edet-Sanson, A.; Vera, P

2009-05-15

The recent introduction of hybrid systems combining a SPECT and a CT in nuclear medicine, greatly improved the diagnostic accuracy for particular clinical indications, due to the possible attenuation and/or scatter correction of the SPECT functional images and the availability of helpful anatomic information. Although the gamma cameras performances are noticeably comparable, the associated CT furnished by the manufacturer are relatively different from each other. Whatever the system is, the introduction of CT in the nuclear diagnostic process results in a significant increase of the patient dose. This dose increase should be justified and optimized considering both the clinical question and the CT settings available on these systems. The installation of a hybrid system must be accompanied by the management of a documentary quality insurance program, jointly developed by the technologists, physicists and physicians, both covering its clinical use and the associated dosimetry issues as monitoring its performances. Particular quality control procedures have to be defined because of the coupling between the two devices. (authors)

A new adaptive hybrid electromagnetic damper: modelling, optimization, and experiment

International Nuclear Information System (INIS)

Asadi, Ehsan; Ribeiro, Roberto; Behrad Khamesee, Mir; Khajepour, Amir

2015-01-01

This paper presents the development of a new electromagnetic hybrid damper which provides regenerative adaptive damping force for various applications. Recently, the introduction of electromagnetic technologies to the damping systems has provided researchers with new opportunities for the realization of adaptive semi-active damping systems with the added benefit of energy recovery. In this research, a hybrid electromagnetic damper is proposed. The hybrid damper is configured to operate with viscous and electromagnetic subsystems. The viscous medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and the capacity for regeneration to the hybrid design. The electromagnetic component is modeled and analyzed using analytical (lumped equivalent magnetic circuit) and electromagnetic finite element method (FEM) (COMSOL ® software package) approaches. By implementing both modeling approaches, an optimization for the geometric aspects of the electromagnetic subsystem is obtained. Based on the proposed electromagnetic hybrid damping concept and the preliminary optimization solution, a prototype is designed and fabricated. A good agreement is observed between the experimental and FEM results for the magnetic field distribution and electromagnetic damping forces. These results validate the accuracy of the modeling approach and the preliminary optimization solution. An analytical model is also presented for viscous damping force, and is compared with experimental results The results show that the damper is able to produce damping coefficients of 1300 and 0–238 N s m −1 through the viscous and electromagnetic components, respectively. (paper)

Development & optimization of a rule-based energy management strategy for fuel economy improvement in hybrid electric vehicles

Science.gov (United States)

Asfoor, Mostafa

The gradual decline of oil reserves and the increasing demand for energy over the past decades has resulted in automotive manufacturers seeking alternative solutions to reduce the dependency on fossil-based fuels for transportation. A viable technology that enables significant improvements in the overall energy conversion efficiencies is the hybridization of conventional vehicle drive systems. This dissertation builds on prior hybrid powertrain development at the University of Idaho. Advanced vehicle models of a passenger car with a conventional powertrain and three different hybrid powertrain layouts were created using GT-Suite. These different powertrain models were validated against a variety of standard driving cycles. The overall fuel economy, energy consumption, and losses were monitored, and a comprehensive energy analysis was performed to compare energy sources and sinks. The GT-Suite model was then used to predict the formula hybrid SAE vehicle performance. Inputs to this model were a numerically predicted engine performance map, an electric motor torque curve, vehicle geometry, and road load parameters derived from a roll-down test. In this case study, the vehicle had a supervisory controller that followed a rule-based energy management strategy to insure a proper power split during hybrid mode operation. The supervisory controller parameters were optimized using discrete grid optimization method that minimized the total amount of fuel consumed during a specific urban driving cycle with an average speed of approximately 30 [mph]. More than a 15% increase in fuel economy was achieved by adding supervisory control and managing power split. The vehicle configuration without the supervisory controller displayed a fuel economy of 25 [mpg]. With the supervisory controller this rose to 29 [mpg]. Wider applications of this research include hybrid vehicle controller designs that can extend the range and survivability of military combat platforms. Furthermore, the

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

Science.gov (United States)

Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

2017-09-01

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

Free chattering hybrid sliding mode control for a class of non-linear systems

DEFF Research Database (Denmark)

Khooban, Mohammad Hassan; Niknam, Taher; Blaabjerg, Frede

2016-01-01

In current study, in order to find the control of general uncertain nonlinear systems, a new optimal hybrid control approach called Optimal General Type II Fuzzy Sliding Mode (OGT2FSM) is presented. In order to estimate unknown nonlinear activities in monitoring dynamic uncertainties, the benefits...... on the same topic, which are an Adaptive Interval Type-2 Fuzzy Logic Controller (AGT2FLC) and Conventional Sliding Mode Controller (CSMC), to assess the efficiency of the suggested controller. The suggested control scheme is finally used to the Electric Vehicles type as a case study. Results of simulation...

Adjoint optimization scheme for lower hybrid current rampup and profile control in Tokamak

International Nuclear Information System (INIS)

Litaudon, X.; Moreau, D.; Bizarro, J.P.; Hoang, G.T.; Kupfer, K.; Peysson, Y.; Shkarofsky, I.P.; Bonoli, P.

1992-12-01

The purpose of this work is to take into account and study the effect of the electric field profiles on the Lower Hybrid (LH) current drive efficiency during transient phases such as rampup. As a complement to the full ray-tracing / Fokker Planck studies, and for the purpose of optimization studies, we developed a simplified 1-D model based on the adjoint Karney-Fisch numerical results. This approach allows us to estimate the LH power deposition profile which would be required for ramping the current with prescribed rate, total current density profile (q-profile) and surface loop voltage. For rampup optimization studies, we can therefore scan the whole parameter space and eliminate a posteriori those scenarios which correspond to unrealistic deposition profiles. We thus obtain the time evolution of the LH power, minor radius of the plasma, volt-second consumption and total energy dissipated. Optimization can thus be performed with respect to any of those criteria. This scheme is illustrated by some numerical simulations performed with TORE-SUPRA and NET/ITER parameters. We conclude with a derivation of a simple and general scaling law for the flux consumption during the rampup phase

Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm

International Nuclear Information System (INIS)

Wang, Jianzhou; Jiang, He; Wu, Yujie; Dong, Yao

2015-01-01

Due to energy crisis and environmental problems, it is very urgent to find alternative energy sources nowadays. Solar energy, as one of the great potential clean energies, has widely attracted the attention of researchers. In this paper, an optimized hybrid method by CS (Cuckoo Search) on the basis of the OP-ELM (Optimally Pruned Extreme Learning Machine), called CS-OP-ELM, is developed to forecast clear sky and real sky global horizontal radiation. First, MRSR (Multiresponse Sparse Regression) and LOO-CV (leave-one-out cross-validation) can be applied to rank neurons and prune the possibly meaningless neurons of the FFNN (Feed Forward Neural Network), respectively. Then, Direct strategy and Direct-Recursive strategy based on OP-ELM are introduced to build a hybrid model. Furthermore, CS (Cuckoo Search) optimized algorithm is employed to determine the proper weight coefficients. In order to verify the effectiveness of the developed method, hourly solar radiation data from six sites of the United States has been collected, and methods like ARMA (Autoregression moving average), BP (Back Propagation) neural network and OP-ELM can be compared with CS-OP-ELM. Experimental results show the optimized hybrid method CS-OP-ELM has the best forecasting performance. - Highlights: • An optimized hybrid method called CS-OP-ELM is proposed to forecast solar radiation. • CS-OP-ELM adopts multiple variables dataset as input variables. • Direct and Direct-Recursive strategy are introduced to build a hybrid model. • CS (Cuckoo Search) algorithm is used to determine the optimal weight coefficients. • The proposed method has the best performance compared with other methods

Hybrid feedforward and feedback controller design for nuclear steam generators over wide range operation using genetic algorithm

International Nuclear Information System (INIS)

Zhao, Y.; Edwards, R.M.; Lee, K.Y.

1997-01-01

In this paper, a simplified model with a lower order is first developed for a nuclear steam generator system and verified against some realistic environments. Based on this simplified model, a hybrid multi-input and multi-out (MIMO) control system, consisting of feedforward control (FFC) and feedback control (FBC), is designed for wide range conditions by using the genetic algorithm (GA) technique. The FFC control, obtained by the GA optimization method, injects an a priori command input into the system to achieve an optimal performance for the designed system, while the GA-based FBC control provides the necessary compensation for any disturbances or uncertainties in a real steam generator. The FBC control is an optimal design of a PI-based control system which would be more acceptable for industrial practices and power plant control system upgrades. The designed hybrid MIMO FFC/FBC control system is first applied to the simplified model and then to a more complicated model with a higher order which is used as a substitute of the real system to test the efficacy of the designed control system. Results from computer simulations show that the designed GA-based hybrid MIMO FFC/FBC control can achieve good responses and robust performances. Hence, it can be considered as a viable alternative to the current control system upgrade

A novel hybrid algorithm of GSA with Kepler algorithm for numerical optimization

Directory of Open Access Journals (Sweden)

Soroor Sarafrazi

2015-07-01

Full Text Available It is now well recognized that pure algorithms can be promisingly improved by hybridization with other techniques. One of the relatively new metaheuristic algorithms is Gravitational Search Algorithm (GSA which is based on the Newton laws. In this paper, to enhance the performance of GSA, a novel algorithm called “Kepler”, inspired by the astrophysics, is introduced. The Kepler algorithm is based on the principle of the first Kepler law. The hybridization of GSA and Kepler algorithm is an efficient approach to provide much stronger specialization in intensification and/or diversification. The performance of GSA–Kepler is evaluated by applying it to 14 benchmark functions with 20–1000 dimensions and the optimal approximation of linear system as a practical optimization problem. The results obtained reveal that the proposed hybrid algorithm is robust enough to optimize the benchmark functions and practical optimization problems.

Battery control system for hybrid vehicle and method for controlling a hybrid vehicle battery

Science.gov (United States)

Bockelmann, Thomas R [Battle Creek, MI; Hope, Mark E [Marshall, MI; Zou, Zhanjiang [Battle Creek, MI; Kang, Xiaosong [Battle Creek, MI

2009-02-10

A battery control system for hybrid vehicle includes a hybrid powertrain battery, a vehicle accessory battery, and a prime mover driven generator adapted to charge the vehicle accessory battery. A detecting arrangement is configured to monitor the vehicle accessory battery's state of charge. A controller is configured to activate the prime mover to drive the generator and recharge the vehicle accessory battery in response to the vehicle accessory battery's state of charge falling below a first predetermined level, or transfer electrical power from the hybrid powertrain battery to the vehicle accessory battery in response to the vehicle accessory battery's state of charge falling below a second predetermined level. The invention further includes a method for controlling a hybrid vehicle powertrain system.

Event-Triggered Distributed Approximate Optimal State and Output Control of Affine Nonlinear Interconnected Systems.

Science.gov (United States)

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-06-08

This paper presents an approximate optimal distributed control scheme for a known interconnected system composed of input affine nonlinear subsystems using event-triggered state and output feedback via a novel hybrid learning scheme. First, the cost function for the overall system is redefined as the sum of cost functions of individual subsystems. A distributed optimal control policy for the interconnected system is developed using the optimal value function of each subsystem. To generate the optimal control policy, forward-in-time, neural networks are employed to reconstruct the unknown optimal value function at each subsystem online. In order to retain the advantages of event-triggered feedback for an adaptive optimal controller, a novel hybrid learning scheme is proposed to reduce the convergence time for the learning algorithm. The development is based on the observation that, in the event-triggered feedback, the sampling instants are dynamic and results in variable interevent time. To relax the requirement of entire state measurements, an extended nonlinear observer is designed at each subsystem to recover the system internal states from the measurable feedback. Using a Lyapunov-based analysis, it is demonstrated that the system states and the observer errors remain locally uniformly ultimately bounded and the control policy converges to a neighborhood of the optimal policy. Simulation results are presented to demonstrate the performance of the developed controller.

A Hybrid Algorithm for Optimizing Multi- Modal Functions

Institute of Scientific and Technical Information of China (English)

Li Qinghua; Yang Shida; Ruan Youlin

2006-01-01

A new genetic algorithm is presented based on the musical performance. The novelty of this algorithm is that a new genetic algorithm, mimicking the musical process of searching for a perfect state of harmony, which increases the robustness of it greatly and gives a new meaning of it in the meantime, has been developed. Combining the advantages of the new genetic algorithm, simplex algorithm and tabu search, a hybrid algorithm is proposed. In order to verify the effectiveness of the hybrid algorithm, it is applied to solving some typical numerical function optimization problems which are poorly solved by traditional genetic algorithms. The experimental results show that the hybrid algorithm is fast and reliable.

Modelling supervisory controller for hybrid power systems

Energy Technology Data Exchange (ETDEWEB)

Pereira, A; Bindner, H; Lundsager, P [Risoe National Lab., Roskilde (Denmark); Jannerup, O [Technical Univ. of Denmark, Dept. of Automation, Lyngby (Denmark)

1999-03-01

Supervisory controllers are important to achieve optimal operation of hybrid power systems. The performance and economics of such systems depend mainly on the control strategy for switching on/off components. The modular concept described in this paper is an attempt to design standard supervisory controllers that could be used in different applications, such as village power and telecommunication applications. This paper presents some basic aspects of modelling and design of modular supervisory controllers using the object-oriented modelling technique. The functional abstraction hierarchy technique is used to formulate the control requirements and identify the functions of the control system. The modular algorithm is generic and flexible enough to be used with any system configuration and several goals (different applications). The modularity includes accepting modification of system configuration and goals during operation with minor or no changes in the supervisory controller. (au)

Modeling and Optimal Control of a Class of Warfare Hybrid Dynamic Systems Based on Lanchester (n,1) Attrition Model

OpenAIRE

Chen, Xiangyong; Zhang, Ancai

2014-01-01

For the particularity of warfare hybrid dynamic process, a class of warfare hybrid dynamic systems is established based on Lanchester equation in a (n,1) battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation), and discrete event systems (described by variable tactics). Furthermore, an expository discussion is presented on an optimal variable tact...

Global Optimal Energy Management Strategy Research for a Plug-In Series-Parallel Hybrid Electric Bus by Using Dynamic Programming

Directory of Open Access Journals (Sweden)

Hongwen He

2013-01-01

Full Text Available Energy management strategy influences the power performance and fuel economy of plug-in hybrid electric vehicles greatly. To explore the fuel-saving potential of a plug-in hybrid electric bus (PHEB, this paper searched the global optimal energy management strategy using dynamic programming (DP algorithm. Firstly, the simplified backward model of the PHEB was built which is necessary for DP algorithm. Then the torque and speed of engine and the torque of motor were selected as the control variables, and the battery state of charge (SOC was selected as the state variables. The DP solution procedure was listed, and the way was presented to find all possible control variables at every state of each stage in detail. Finally, the appropriate SOC increment is determined after quantizing the state variables, and then the optimal control of long driving distance of a specific driving cycle is replaced with the optimal control of one driving cycle, which reduces the computational time significantly and keeps the precision at the same time. The simulation results show that the fuel economy of the PEHB with the optimal energy management strategy is improved by 53.7% compared with that of the conventional bus, which can be a benchmark for the assessment of other control strategies.

An Aerial Robot for Rice Farm Quality Inspection With Type-2 Fuzzy Neural Networks Tuned by Particle Swarm Optimization-Sliding Mode Control Hybrid Algorithm

DEFF Research Database (Denmark)

Camci, Efe; Kripalan, Devesh Raju; Ma, Linlu

2017-01-01

, an autonomous quality inspection over rice farms is proposed by employing quadcopters. Real-time control of these vehicles, however, is still challenging as they exhibit highly nonlinear behavior especially for agile maneuvers. What is more, these vehicles have to operate under uncertain working conditions...... particle swarm optimization-sliding mode control (PSO-SMC) theory-based hybrid algorithm is proposed for the training of T2-FNNs. In particular, continuous version of PSO is adopted for the identification of the antecedent part of T2-FNNs while SMCbased update rules are utilized for online learning...

Solar-Diesel Hybrid Power System Optimization and Experimental Validation

Science.gov (United States)

Jacobus, Headley Stewart

As of 2008 1.46 billion people, or 22 percent of the World's population, were without electricity. Many of these people live in remote areas where decentralized generation is the only method of electrification. Most mini-grids are powered by diesel generators, but new hybrid power systems are becoming a reliable method to incorporate renewable energy while also reducing total system cost. This thesis quantifies the measurable Operational Costs for an experimental hybrid power system in Sierra Leone. Two software programs, Hybrid2 and HOMER, are used during the system design and subsequent analysis. Experimental data from the installed system is used to validate the two programs and to quantify the savings created by each component within the hybrid system. This thesis bridges the gap between design optimization studies that frequently lack subsequent validation and experimental hybrid system performance studies.

Performance of a Nonlinear Real-Time Optimal Control System for HEVs/PHEVs during Car Following

Directory of Open Access Journals (Sweden)

Kaijiang Yu

2014-01-01

Full Text Available This paper presents a real-time optimal control approach for the energy management problem of hybrid electric vehicles (HEVs and plug-in hybrid electric vehicles (PHEVs with slope information during car following. The new features of this study are as follows. First, the proposed method can optimize the engine operating points and the driving profile simultaneously. Second, the proposed method gives the freedom of vehicle spacing between the preceding vehicle and the host vehicle. Third, using the HEV/PHEV property, the desired battery state of charge is designed according to the road slopes for better recuperation of free braking energy. Fourth, all of the vehicle operating modes engine charge, electric vehicle, motor assist and electric continuously variable transmission, and regenerative braking, can be realized using the proposed real-time optimal control approach. Computer simulation results are shown among the nonlinear real-time optimal control approach and the ADVISOR rule-based approach. The conclusion is that the nonlinear real-time optimal control approach is effective for the energy management problem of the HEV/PHEV system during car following.

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

Science.gov (United States)

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

Science.gov (United States)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

HYBRID VEHICLE CONTROL SYSTEM

Directory of Open Access Journals (Sweden)

V. Dvadnenko

2016-06-01

Full Text Available The hybrid vehicle control system includes a start–stop system for an internal combustion engine. The system works in a hybrid mode and normal vehicle operation. To simplify the start–stop system, there were user new possibilities of a hybrid car, which appeared after the conversion. Results of the circuit design of the proposed system of basic blocks are analyzed.

Hybrid chaotic ant swarm optimization

International Nuclear Information System (INIS)

Li Yuying; Wen Qiaoyan; Li Lixiang; Peng Haipeng

2009-01-01

Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm that is used to find the global optimum solution in search space. However, the CASO algorithm has some disadvantages, such as lower solution precision and longer computational time, when solving complex optimization problems. To resolve these problems, an improved CASO, called hybrid chaotic swarm optimization (HCASO), is proposed in this paper. The new algorithm introduces preselection operator and discrete recombination operator into the CASO; meanwhile it replaces the best position found by own and its neighbors' ants with the best position found by preselection operator and discrete recombination operator in evolution equation. Through testing five benchmark functions with large dimensionality, the experimental results show the new method enhances the solution accuracy and stability greatly, as well as reduces the computational time and computer memory significantly when compared to the CASO. In addition, we observe the results can become better with swarm size increasing from the sensitivity study to swarm size. And we gain some relations between problem dimensions and swam size according to scalability study.

Optimal control of Formula One car energy recovery systems

Science.gov (United States)

Limebeer, D. J. N.; Perantoni, G.; Rao, A. V.

2014-10-01

The utility of orthogonal collocation methods in the solution of optimal control problems relating to Formula One racing is demonstrated. These methods can be used to optimise driver controls such as the steering, braking and throttle usage, and to optimise vehicle parameters such as the aerodynamic down force and mass distributions. Of particular interest is the optimal usage of energy recovery systems (ERSs). Contemporary kinetic energy recovery systems are studied and compared with future hybrid kinetic and thermal/heat ERSs known as ERS-K and ERS-H, respectively. It is demonstrated that these systems, when properly controlled, can produce contemporary lap time using approximately two-thirds of the fuel required by earlier generation (2013 and prior) vehicles.

A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

Directory of Open Access Journals (Sweden)

Lijin Wang

2015-01-01

Full Text Available The backtracking search optimization algorithm (BSA is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.

Mass Optimization of Battery/Supercapacitors Hybrid Systems Based on a Linear Programming Approach

Science.gov (United States)

Fleury, Benoit; Labbe, Julien

2014-08-01

The objective of this paper is to show that, on a specific launcher-type mission profile, a 40% gain of mass is expected using a battery/supercapacitors active hybridization instead of a single battery solution. This result is based on the use of a linear programming optimization approach to perform the mass optimization of the hybrid power supply solution.

Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

Science.gov (United States)

2013-12-01

with 214 turbines [22]. In July 2011, the DoD declared that a complete study of 217 wind farm projects proposed in 35 states and Puerto Rico found...14. SUBJECT TERMS Hybrid electric grid , Microgrid , Hybrid renewable energy system , energy management center, optimization, Day...electric grid. In the case of a hybrid electric grid (HEG), or hybrid renewable energy system (HRES) where the microgrid can be connected to the commercial

Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

Directory of Open Access Journals (Sweden)

Zunaira Nadeem

2018-04-01

Full Text Available In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO, genetic algorithm (GA, firefly algorithm (FA and optimal stopping rule (OSR theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.

A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries

International Nuclear Information System (INIS)

Suri, Girish; Onori, Simona

2016-01-01

In this paper, a semi-empirical Lithium-iron phosphate-graphite battery aging model is identified over data mimicking actual cycling conditions that a hybrid electric vehicle battery encounters under real driving scenarios. The aging model is then used to construct the severity factor map, used to characterize relative aging of the battery under different operating conditions. This is used as a battery degradation criterion within a multi-objective optimization problem where battery aging minimization is to be achieved along with fuel consumption minimization. The method proposed is general and can be applied to other battery chemistry as well as different vehicular applications. Finally, simulations conducted using a hybrid electric vehicle simulator show how the two modeling tools developed in this paper, i.e., the severity factor map and the aging model, can be effectively used in a multi-objective optimization problem to predict and control battery degradation. - Highlights: • Battery aging model for hybrid electric vehicles using real driving conditions data. • Development of a modeling tool to assess battery degradation for real time optimization. • "3"1P NMR analysis of an enzyme-treated extract showed expected hydrolysis of P forms. • Development of an energy management strategy to minimize battery degradation. • Simulation results from hybrid electric vehicle simulator.

Optimizing the specificity of nucleic acid hybridization.

Science.gov (United States)

Zhang, David Yu; Chen, Sherry Xi; Yin, Peng

2012-01-22

The specific hybridization of complementary sequences is an essential property of nucleic acids, enabling diverse biological and biotechnological reactions and functions. However, the specificity of nucleic acid hybridization is compromised for long strands, except near the melting temperature. Here, we analytically derived the thermodynamic properties of a hybridization probe that would enable near-optimal single-base discrimination and perform robustly across diverse temperature, salt and concentration conditions. We rationally designed 'toehold exchange' probes that approximate these properties, and comprehensively tested them against five different DNA targets and 55 spurious analogues with energetically representative single-base changes (replacements, deletions and insertions). These probes produced discrimination factors between 3 and 100+ (median, 26). Without retuning, our probes function robustly from 10 °C to 37 °C, from 1 mM Mg(2+) to 47 mM Mg(2+), and with nucleic acid concentrations from 1 nM to 5 µM. Experiments with RNA also showed effective single-base change discrimination.

Lipid-polymer hybrid nanoparticles: Development & statistical optimization of norfloxacin for topical drug delivery system

Directory of Open Access Journals (Sweden)

Vivek Dave

2017-12-01

Full Text Available Poly lactic acid is a biodegradable, biocompatible, and non-toxic polymer, widely used in many pharmaceutical preparations such as controlled release formulations, parenteral preparations, surgical treatment applications, and tissue engineering. In this study, we prepared lipid-polymer hybrid nanoparticles for topical and site targeting delivery of Norfloxacin by emulsification solvent evaporation method (ESE. The design of experiment (DOE was done by using software to optimize the result, and then a surface plot was generated to compare with the practical results. The surface morphology, particle size, zeta potential and composition of the lipid-polymer hybrid nanoparticles were characterized by SEM, TEM, AFM, and FTIR. The thermal behavior of the lipid-polymer hybrid nanoparticles was characterized by DSC and TGA. The prepared lipid-polymer hybrid nanoparticles of Norfloxacin exhibited an average particle size from 178.6 ± 3.7 nm to 220.8 ± 2.3 nm, and showed very narrow distribution with polydispersity index ranging from 0.206 ± 0.36 to 0.383 ± 0.66. The surface charge on the lipid-polymer hybrid nanoparticles were confirmed by zeta potential, showed the value from +23.4 ± 1.5 mV to +41.5 ± 3.4 mV. An Antimicrobial study was done against Staphylococcus aureus and Pseudomonas aeruginosa, and the lipid-polymer hybrid nanoparticles showed potential activity against these two. Lipid-polymer hybrid nanoparticles of Norfloxacin showed the %cumulative drug release of 89.72% in 24 h. A stability study of the optimized formulation showed the suitable condition for the storage of lipid-polymer hybrid nanoparticles was at 4 ± 2 °C/60 ± 5% RH. These results illustrated high potential of lipid-polymer hybrid nanoparticles Norfloxacin for usage as a topical antibiotic drug carriers.

Hybrid Optimization for Wind Turbine Thick Airfoils

Energy Technology Data Exchange (ETDEWEB)

Grasso, F. [ECN Wind Energy, Petten (Netherlands)

2012-06-15

One important element in aerodynamic design of wind turbines is the use of specially tailored airfoils to increase the ratio of energy capture and reduce cost of energy. This work is focused on the design of thick airfoils for wind turbines by using numerical optimization. A hybrid scheme is proposed in which genetic and gradient based algorithms are combined together to improve the accuracy and the reliability of the design. Firstly, the requirements and the constraints for this class of airfoils are described; then, the hybrid approach is presented. The final part of this work is dedicated to illustrate a numerical example regarding the design of a new thick airfoil. The results are discussed and compared to existing airfoils.

Hybrid spacecraft attitude control system

OpenAIRE

Renuganth Varatharajoo; Ramly Ajir; Tamizi Ahmad

2016-01-01

The hybrid subsystem design could be an attractive approach for futurespacecraft to cope with their demands. The idea of combining theconventional Attitude Control System and the Electrical Power System ispresented in this article. The Combined Energy and Attitude ControlSystem (CEACS) consisting of a double counter rotating flywheel assemblyis investigated for small satellites in this article. Another hybrid systemincorporating the conventional Attitude Control System into the ThermalControl...

A methodology for optimal sizing of autonomous hybrid PV/wind system

International Nuclear Information System (INIS)

Diaf, S.; Diaf, D.; Belhamel, M.; Haddadi, M.; Louche, A.

2007-01-01

The present paper presents a methodology to perform the optimal sizing of an autonomous hybrid PV/wind system. The methodology aims at finding the configuration, among a set of systems components, which meets the desired system reliability requirements, with the lowest value of levelized cost of energy. Modelling a hybrid PV/wind system is considered as the first step in the optimal sizing procedure. In this paper, more accurate mathematical models for characterizing PV module, wind generator and battery are proposed. The second step consists to optimize the sizing of a system according to the loss of power supply probability (LPSP) and the levelized cost of energy (LCE) concepts. Considering various types and capacities of system devices, the configurations, which can meet the desired system reliability, are obtained by changing the type and size of the devices systems. The configuration with the lowest LCE gives the optimal choice. Applying this method to an assumed PV/wind hybrid system to be installed at Corsica Island, the simulation results show that the optimal configuration, which meet the desired system reliability requirements (LPSP=0) with the lowest LCE, is obtained for a system comprising a 125 W photovoltaic module, one wind generator (600 W) and storage batteries (using 253 Ah). On the other hand, the device system choice plays an important role in cost reduction as well as in energy production

Study on hybrid multi-objective optimization algorithm for inverse treatment planning of radiation therapy

International Nuclear Information System (INIS)

Li Guoli; Song Gang; Wu Yican

2007-01-01

Inverse treatment planning for radiation therapy is a multi-objective optimization process. The hybrid multi-objective optimization algorithm is studied by combining the simulated annealing(SA) and genetic algorithm(GA). Test functions are used to analyze the efficiency of algorithms. The hybrid multi-objective optimization SA algorithm, which displacement is based on the evolutionary strategy of GA: crossover and mutation, is implemented in inverse planning of external beam radiation therapy by using two kinds of objective functions, namely the average dose distribution based and the hybrid dose-volume constraints based objective functions. The test calculations demonstrate that excellent converge speed can be achieved. (authors)

Optimal sizing study of hybrid wind/PV/diesel power generation unit

Energy Technology Data Exchange (ETDEWEB)

Belfkira, Rachid; Zhang, Lu; Barakat, Georges [Groupe de Recherche en Electrotechnique et Automatique du Havre, University of Le Havre, 25 rue Philippe Lebon, BP 1123, 76063 Le Havre (France)

2011-01-15

In this paper, a methodology of sizing optimization of a stand-alone hybrid wind/PV/diesel energy system is presented. This approach makes use of a deterministic algorithm to suggest, among a list of commercially available system devices, the optimal number and type of units ensuring that the total cost of the system is minimized while guaranteeing the availability of the energy. The collection of 6 months of data of wind speed, solar radiation and ambient temperature recorded for every hour of the day were used. The mathematical modeling of the main elements of the hybrid wind/PV/diesel system is exposed showing the more relevant sizing variables. A deterministic algorithm is used to minimize the total cost of the system while guaranteeing the satisfaction of the load demand. A comparison between the total cost of the hybrid wind/PV/diesel energy system with batteries and the hybrid wind/PV/diesel energy system without batteries is presented. The reached results demonstrate the practical utility of the used sizing methodology and show the influence of the battery storage on the total cost of the hybrid system. (author)

A Hybrid Optimization Algorithm for Low RCS Antenna Design

Directory of Open Access Journals (Sweden)

W. Shao

2012-12-01

Full Text Available In this article, a simple and efficient method is presented to design low radar cross section (RCS patch antennas. This method consists of a hybrid optimization algorithm, which combines a genetic algorithm (GA with tabu search algorithm (TSA, and electromagnetic field solver. The TSA, embedded into the GA frame, defines the acceptable neighborhood region of parameters and screens out the poor-scoring individuals. Thus, the repeats of search are avoided and the amount of time-consuming electromagnetic simulations is largely reduced. Moreover, the whole design procedure is auto-controlled by programming the VBScript language. A slot patch antenna example is provided to verify the accuracy and efficiency of the proposed method.

Optimized Hybrid Renewable Energy System of Isolated Islands in Smart-Grid Scenario - A Case Study in Indian Context

OpenAIRE

Aurobi Das; V. Balakrishnan

2012-01-01

This paper focuses on the integration of hybrid renewable energy resources available in remote isolated islands of Sundarban-24 Parganas-South of Eastern part of India to National Grid of conventional power supply to give a Smart-Grid scenario. Before grid-integration, feasibility of optimization of hybrid renewable energy system is monitored through an Intelligent Controller proposed to be installed at Moushuni Island of Sundarban. The objective is to ensure the reliability and efficiency of...

A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

Science.gov (United States)

Tong, Cao; Gong, Haili

2018-03-01

This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.

Hybrid electric vehicles energy management strategies

CERN Document Server

Onori, Simona; Rizzoni, Giorgio

2016-01-01

This SpringerBrief deals with the control and optimization problem in hybrid electric vehicles. Given that there are two (or more) energy sources (i.e., battery and fuel) in hybrid vehicles, it shows the reader how to implement an energy-management strategy that decides how much of the vehicle’s power is provided by each source instant by instant. Hybrid Electric Vehicles: •introduces methods for modeling energy flow in hybrid electric vehicles; •presents a standard mathematical formulation of the optimal control problem; •discusses different optimization and control strategies for energy management, integrating the most recent research results; and •carries out an overall comparison of the different control strategies presented. Chapter by chapter, a case study is thoroughly developed, providing illustrative numerical examples that show the basic principles applied to real-world situations. In addition to the examples, simulation code is provided via a website, so that readers can work on the actua...

A novel power control strategy of Modular Multi-level Converter in HVDC-AC hybrid transmission systems for passive networks

DEFF Research Database (Denmark)

Hu, Zhenda; Wu, Rui; Yang, Xiaodong

2014-01-01

With the development of High Voltage DC Transmission (HVDC) technology, there will be more and more HVDC-AC hybrid transmission system in the world. A basic challenge in HVDC-AC hybrid transmission systems is to optimize the power sharing between DC and AC lines, which become more severe when sup...... control strategy of Modular Multi-level Converter in VSC-HVDC, which can optimize converter output power according to passive network loading variation. Proposal method is studied with a case study of a VSC-HVDC AC hybrid project by PSCAD/EMTDC simulations....

Power-balancing instantaneous optimization energy management for a novel series-parallel hybrid electric bus

Science.gov (United States)

Sun, Dongye; Lin, Xinyou; Qin, Datong; Deng, Tao

2012-11-01

Energy management(EM) is a core technique of hybrid electric bus(HEB) in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization(PBIO) energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus(SPHEB) dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.

Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm

Directory of Open Access Journals (Sweden)

V. D. Sulimov

2014-01-01

Full Text Available Modern methods for optimization investigation of complex systems are based on development and updating the mathematical models of systems because of solving the appropriate inverse problems. Input data desirable for solution are obtained from the analysis of experimentally defined consecutive characteristics for a system or a process. Causal characteristics are the sought ones to which equation coefficients of mathematical models of object, limit conditions, etc. belong. The optimization approach is one of the main ones to solve the inverse problems. In the main case it is necessary to find a global extremum of not everywhere differentiable criterion function. Global optimization methods are widely used in problems of identification and computation diagnosis system as well as in optimal control, computing to-mography, image restoration, teaching the neuron networks, other intelligence technologies. Increasingly complicated systems of optimization observed during last decades lead to more complicated mathematical models, thereby making solution of appropriate extreme problems significantly more difficult. A great deal of practical applications may have the problem con-ditions, which can restrict modeling. As a consequence, in inverse problems the criterion functions can be not everywhere differentiable and noisy. Available noise means that calculat-ing the derivatives is difficult and unreliable. It results in using the optimization methods without calculating the derivatives.An efficiency of deterministic algorithms of global optimization is significantly restrict-ed by their dependence on the extreme problem dimension. When the number of variables is large they use the stochastic global optimization algorithms. As stochastic algorithms yield too expensive solutions, so this drawback restricts their applications. Developing hybrid algo-rithms that combine a stochastic algorithm for scanning the variable space with deterministic local search

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

Directory of Open Access Journals (Sweden)

Nadeem Javaid

2017-03-01

Full Text Available In recent years, demand side management (DSM techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA, the binary particle swarm optimization (BPSO algorithm, the bacterial foraging optimization algorithm (BFOA, the wind-driven optimization (WDO algorithm and our proposed hybrid genetic wind-driven (GWD algorithm are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs and off-peak hours (OPHs in a real-time pricing (RTP environment while maximizing user comfort (UC and minimizing both electricity cost and the peak to average ratio (PAR. Moreover, these algorithms are tested in two scenarios: (i scheduling the load of a single home and (ii scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

Microwave imaging for conducting scatterers by hybrid particle swarm optimization with simulated annealing

International Nuclear Information System (INIS)

Mhamdi, B.; Grayaa, K.; Aguili, T.

2011-01-01

In this paper, a microwave imaging technique for reconstructing the shape of two-dimensional perfectly conducting scatterers by means of a stochastic optimization approach is investigated. Based on the boundary condition and the measured scattered field derived by transverse magnetic illuminations, a set of nonlinear integral equations is obtained and the imaging problem is reformulated in to an optimization problem. A hybrid approximation algorithm, called PSO-SA, is developed in this work to solve the scattering inverse problem. In the hybrid algorithm, particle swarm optimization (PSO) combines global search and local search for finding the optimal results assignment with reasonable time and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of SA. Reconstruction results are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed.

Multi-objective decoupling algorithm for active distance control of intelligent hybrid electric vehicle

Science.gov (United States)

Luo, Yugong; Chen, Tao; Li, Keqiang

2015-12-01

The paper presents a novel active distance control strategy for intelligent hybrid electric vehicles (IHEV) with the purpose of guaranteeing an optimal performance in view of the driving functions, optimum safety, fuel economy and ride comfort. Considering the complexity of driving situations, the objects of safety and ride comfort are decoupled from that of fuel economy, and a hierarchical control architecture is adopted to improve the real-time performance and the adaptability. The hierarchical control structure consists of four layers: active distance control object determination, comprehensive driving and braking torque calculation, comprehensive torque distribution and torque coordination. The safety distance control and the emergency stop algorithms are designed to achieve the safety and ride comfort goals. The optimal rule-based energy management algorithm of the hybrid electric system is developed to improve the fuel economy. The torque coordination control strategy is proposed to regulate engine torque, motor torque and hydraulic braking torque to improve the ride comfort. This strategy is verified by simulation and experiment using a forward simulation platform and a prototype vehicle. The results show that the novel control strategy can achieve the integrated and coordinated control of its multiple subsystems, which guarantees top performance of the driving functions and optimum safety, fuel economy and ride comfort.

Optimal design of damping layers in SMA/GFRP laminated hybrid composites

Science.gov (United States)

Haghdoust, P.; Cinquemani, S.; Lo Conte, A.; Lecis, N.

2017-10-01

This work describes the optimization of the shape profiles for shape memory alloys (SMA) sheets in hybrid layered composite structures, i.e. slender beams or thinner plates, designed for the passive attenuation of flexural vibrations. The paper starts with the description of the material and architecture of the investigated hybrid layered composite. An analytical method, for evaluating the energy dissipation inside a vibrating cantilever beam is developed. The analytical solution is then followed by a shape profile optimization of the inserts, using a genetic algorithm to minimize the SMA material layer usage, while maintaining target level of structural damping. Delamination problem at SMA/glass fiber reinforced polymer interface is discussed. At the end, the proposed methodology has been applied to study the hybridization of a wind turbine layered structure blade with SMA material, in order to increase its passive damping.

NEURAL NETWORKS CONTROL OF THE HYBRID POWER UNIT BASED ON THE METHOD OF ADAPTIVE CRITICS

Directory of Open Access Journals (Sweden)

S. Serikov

2012-01-01

Full Text Available The formal statement of the optimization problem of hybrid vehicle power unit control is given. Its solving by neural networks method application on the basis of adaptive critic is considered.

Optimal power flow: a bibliographic survey II. Non-deterministic and hybrid methods

Energy Technology Data Exchange (ETDEWEB)

Frank, Stephen [Colorado School of Mines, Department of Electrical Engineering and Computer Science, Golden, CO (United States); Steponavice, Ingrida [Univ. of Jyvaskyla, Dept. of Mathematical Information Technology, Agora (Finland); Rebennack, Steffen [Colorado School of Mines, Division of Economics and Business, Golden, CO (United States)

2012-09-15

Over the past half-century, optimal power flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey (this article) examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF. (orig.)

Design Optimization of a Hybrid Electric Vehicle Powertrain

Science.gov (United States)

Mangun, Firdause; Idres, Moumen; Abdullah, Kassim

2017-03-01

This paper presents an optimization work on hybrid electric vehicle (HEV) powertrain using Genetic Algorithm (GA) method. It focused on optimization of the parameters of powertrain components including supercapacitors to obtain maximum fuel economy. Vehicle modelling is based on Quasi-Static-Simulation (QSS) backward-facing approach. A combined city (FTP-75)-highway (HWFET) drive cycle is utilized for the design process. Seeking global optimum solution, GA was executed with different initial settings to obtain sets of optimal parameters. Starting from a benchmark HEV, optimization results in a smaller engine (2 l instead of 3 l) and a larger battery (15.66 kWh instead of 2.01 kWh). This leads to a reduction of 38.3% in fuel consumption and 30.5% in equivalent fuel consumption. Optimized parameters are also compared with actual values for HEV in the market.

Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus

International Nuclear Information System (INIS)

Hu, Xiaosong; Johannesson, Lars; Murgovski, Nikolce; Egardt, Bo

2015-01-01

Highlights: • Hybrid energy storage system is optimally sized and controlled for a hybrid bus. • Dynamic battery health model is incorporated in the optimization. • Convex programming is efficient for optimizing hybrid propulsion systems. • Optimal battery replacement strategy is explored. • Comparison to the battery-only option is made in the health-aware optimization. - Abstract: Energy storage systems (ESSs) play an important role in the performance and economy of electrified vehicles. Hybrid energy storage system (HESS) combining both lithium-ion cells and supercapacitors is one of the most promising solutions. This paper discusses the optimal HESS dimensioning and energy management of a fuel cell hybrid electric bus. Three novel contributions are added to the relevant literature. First, efficient convex programming is used to simultaneously optimize the HESS dimension (including sizes of both the lithium-ion battery pack and the supercapacitor stack) and the power allocation between the HESS and the fuel cell system (FCS) of the hybrid bus. In the combined plant/controller optimization problem, a dynamic battery State-of-Health (SOH) model is integrated to quantitatively examine the impact of the battery replacement strategy on both the HESS size and the bus economy. Second, the HESS and the battery-only ESS options are systematically compared in the proposed optimization framework. Finally, the battery-health-perceptive HESS optimization outcome is contrasted to the ideal one neglecting the battery degradation (assuming that the battery is durable over the bus service period without deliberate power regulation)

Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses

Directory of Open Access Journals (Sweden)

Hanho Son

2016-05-01

Full Text Available A near-optimal rule-based mode control (RBC strategy was proposed for a target plug-in hybrid electric vehicle (PHEV taking into account the drivetrain losses. Individual loss models were developed for drivetrain components including the gears, planetary gear (PG, bearings, and oil pump, based on experimental data and mathematical governing equations. Also, a loss model for the power electronic system was constructed, including loss from the motor-generator while rotating in the unloaded state. To evaluate the effect of the drivetrain losses on the operating mode control strategy, backward simulations were performed using dynamic programming (DP. DP selects the operating mode, which provides the highest efficiency for given driving conditions. It was found that the operating mode selection changes when drivetrain losses are included, depending on driving conditions. An operating mode schedule was developed with respect to the wheel power and vehicle speed, and based on the operating mode schedule, a RBC was obtained, which can be implemented in an on-line application. To evaluate the performance of the RBC, a forward simulator was constructed for the target PHEV. The simulation results show near-optimal performance of the RBC compared with dynamic-programming-based mode control in terms of the mode operation time and fuel economy. The RBC developed with drivetrain losses taken into account showed a 4%–5% improvement of the fuel economy over a similar RBC, which neglected the drivetrain losses.

A hybrid algorithm for instant optimization of beam weights in anatomy-based intensity modulated radiotherapy: a performance evaluation study

International Nuclear Information System (INIS)

Vaitheeswaran, Ranganathan; Sathiya Narayanan, V.K.; Bhangle, Janhavi R.; Nirhali, Amit; Kumar, Namita; Basu, Sumit; Maiya, Vikram

2011-01-01

The study aims to introduce a hybrid optimization algorithm for anatomy-based intensity modulated radiotherapy (AB-IMRT). Our proposal is that by integrating an exact optimization algorithm with a heuristic optimization algorithm, the advantages of both the algorithms can be combined, which will lead to an efficient global optimizer solving the problem at a very fast rate. Our hybrid approach combines Gaussian elimination algorithm (exact optimizer) with fast simulated annealing algorithm (a heuristic global optimizer) for the optimization of beam weights in AB-IMRT. The algorithm has been implemented using MATLAB software. The optimization efficiency of the hybrid algorithm is clarified by (i) analysis of the numerical characteristics of the algorithm and (ii) analysis of the clinical capabilities of the algorithm. The numerical and clinical characteristics of the hybrid algorithm are compared with Gaussian elimination method (GEM) and fast simulated annealing (FSA). The numerical characteristics include convergence, consistency, number of iterations and overall optimization speed, which were analyzed for the respective cases of 8 patients. The clinical capabilities of the hybrid algorithm are demonstrated in cases of (a) prostate and (b) brain. The analyses reveal that (i) the convergence speed of the hybrid algorithm is approximately three times higher than that of FSA algorithm (ii) the convergence (percentage reduction in the cost function) in hybrid algorithm is about 20% improved as compared to that in GEM algorithm (iii) the hybrid algorithm is capable of producing relatively better treatment plans in terms of Conformity Index (CI) (∼ 2% - 5% improvement) and Homogeneity Index (HI) (∼ 4% - 10% improvement) as compared to GEM and FSA algorithms (iv) the sparing of organs at risk in hybrid algorithm-based plans is better than that in GEM-based plans and comparable to that in FSA-based plans; and (v) the beam weights resulting from the hybrid algorithm are

Hybrid K-means Dan Particle Swarm Optimization Untuk Clustering Nasabah Kredit

Directory of Open Access Journals (Sweden)

Yusuf Priyo Anggodo

2017-05-01

Credit is the biggest revenue for the bank. However, banks have to be selective in deciding which clients can receive the credit. This issue is becoming increasingly complex because when the bank was wrong to give credit to customers can do harm, apart of that a large number of deciding parameter in determining customer credit. Clustering is one way to be able to resolve this issue. K-means is a simple and popular method for solving clustering. However, K-means pure can’t provide optimum solutions so that needs to be done to get the optimum solution to improve. One method of optimization that can solve the problems of optimization with particle swarm optimization is good (PSO. PSO is very helpful in the process of clustering to perform optimization on the central point of each cluster. To improve better results on PSO there are some that do improve. The first use of time-variant inertia to make the dynamic value of inertial w each iteration. Both control the speed of the particle velocity or clamping to get the best position. Besides to overcome premature convergence do hybrid PSO with random injection. The results of this research provide the optimum results for solving clustering of customer credits. The test results showed the hybrid PSO K-means provide the greatest results than K-means and PSO K-means, where the silhouette of the K-means, PSO K-means, and hybrid PSO K-means respectively 0.57343, 0.792045, 1. Keywords: Credit, Clustering, PSO, K-means, Random Injection

Test Beam Results of Geometry Optimized Hybrid Pixel Detectors

CERN Document Server

Becks, K H; Grah, C; Mättig, P; Rohe, T

2006-01-01

The Multi-Chip-Module-Deposited (MCM-D) technique has been used to build hybrid pixel detector assemblies. This paper summarises the results of an analysis of data obtained in a test beam campaign at CERN. Here, single chip hybrids made of ATLAS pixel prototype read-out electronics and special sensor tiles were used. They were prepared by the Fraunhofer Institut fuer Zuverlaessigkeit und Mikrointegration, IZM, Berlin, Germany. The sensors feature an optimized sensor geometry called equal sized bricked. This design enhances the spatial resolution for double hits in the long direction of the sensor cells.

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

Science.gov (United States)

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

Directory of Open Access Journals (Sweden)

Jun-qing Li

2014-01-01

Full Text Available A hybrid algorithm which combines particle swarm optimization (PSO and iterated local search (ILS is proposed for solving the hybrid flowshop scheduling (HFS problem with preventive maintenance (PM activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron’s benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

Hybrid particle swarm optimization for hybrid flowshop scheduling problem with maintenance activities.

Science.gov (United States)

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

Optimum Performance-Based Seismic Design Using a Hybrid Optimization Algorithm

Directory of Open Access Journals (Sweden)

S. Talatahari

2014-01-01

Full Text Available A hybrid optimization method is presented to optimum seismic design of steel frames considering four performance levels. These performance levels are considered to determine the optimum design of structures to reduce the structural cost. A pushover analysis of steel building frameworks subject to equivalent-static earthquake loading is utilized. The algorithm is based on the concepts of the charged system search in which each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Comparison of the results of the hybrid algorithm with those of other metaheuristic algorithms shows the efficiency of the hybrid algorithm.

A Semiactive and Adaptive Hybrid Control System for a Tracked Vehicle Hydropneumatic Suspension Based on Disturbance Identification

Directory of Open Access Journals (Sweden)

Shousong Han

2017-01-01

Full Text Available The riding conditions for a high-speed tracked vehicle are quite complex. To enhance the adaptability of suspension systems to different riding conditions, a semiactive and self-adaptive hybrid control strategy based on disturbance velocity and frequency identification was proposed. A mathematical model of the semiactive, self-adaptive hybrid suspension control system, along with a performance evaluation function, was established. Based on a two-degree-of-freedom (DOF suspension system, the kinematic relations and frequency zero-crossing detection method were defined, and expressions for the disturbance velocity and disturbance frequency of the road were obtained. Optimal scheduling of the semiactive hybrid damping control gain (csky, cground, chybrid and self-adaptive control gain (cv under different disturbances were realized by exploiting the particle swarm multiobjective optimization algorithm. An experimental study using a carefully designed test rig was performed under a number of typical riding conditions of tracked vehicles, and the results showed that the proposed control strategy is capable of accurately recognizing different disturbances, shifting between control modes (semiactive/self-adaptive, and scheduling the damping control gain according to the disturbance identification outcomes; hence, the proposed strategy could achieve a good trade-off between ride comfort and ride safety and efficiently increase the overall performance of the suspension under various riding conditions.

Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO.

Science.gov (United States)

Pan, Indranil; Das, Saptarshi

2016-05-01

This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Evaluation of Manufacturing Process Performance by CONWIP Hybridization of Pull Controlled Production Systems

Directory of Open Access Journals (Sweden)

Srikanth O.

2018-01-01

Full Text Available The main objective of this paper is pioneering an innovative tactic for the synchronization of multi-stage, multi-line, production system. This tactic is mainly depends on the optimization policy, by means of distinct event simulation process for modeling, analysis and distinction of the execution of two alternatives of Kanban control mechanism namely SEKCS (Simultaneous Extended Kanban Control System and IEKCS (Independent Extended Kanban Control System. At this juncture the authors putting forward the two variants of Extended Kanban control system with the hybridization of CONWIP control policy to incite HSEKCS (Hybrid Simultaneous Extended Kanban Control System and HIEKCS (Hybrid Independent Extended Kanban Control System to make use of pooled benefits of a representative production situation in addition to improve the outcome. Therefore in this study the comparison in between different systems of proposed HEKCS specifically are HSEKCS and HIEKCS compared with the Extended Kanban Control Systems variants SEKCS and IEKCS. Simulation studies were conducted for all the five control policies considered and modeled on a multi-line, multi-stage assembly production control system. The relative performance parameters like Throughput or Production rate, Average Waiting Time and Average Work-in-Process, were assessed by means of exponentially varying demands.

Optimal powertrain dimensioning and potential assessment of hybrid electric vehicles

Energy Technology Data Exchange (ETDEWEB)

Murgovski, Nikolce

2012-07-01

Hybrid electric vehicles (HEVs), compared to conventional vehicles, complement the traditional combustion engine with one, or several electric motors and an energy buffer, typically a battery and/or an ultra capacitor. This gives the vehicle an additional degree of freedom that allows for a more efficient operation, by e.g. recuperating braking energy, or operating the engine at higher efficiency. In order to be cost effective, the HEV may need to include a downsized engine and a carefully selected energy buffer. The optimal size of the powertrain components depends on the powertrain configuration, ability to draw electric energy from the grid, charging infrastructure, drive patterns, varying fuel, electricity and energy buffer prices and on how well adapted is the buffer energy management to driving conditions. This thesis provides two main contributions for optimal dimensioning of HEV powertrains while optimally controlling the energy use of the buffer on prescribed routes. The first contribution is described by a methodology and a tool for potential assessment of HEV powertrains. The tool minimizes the need for interaction from the user by automizing the processes of powertrain simplification and optimization. The HEV powertrain models are simplified by removing unnecessary dynamics in order to speed up computation time and allow Dynamic Programming to be used to optimize the energy management. The tool makes it possible to work with non-transparent models, e.g. models which are compiled, or hidden for intellectual property reasons. The second contribution describes modeling steps to reformulate the powertrain dimensioning and control problem as a convex optimization problem. The method considers quadratic losses for the powertrain components and the resulting problem is a semi definite convex program. The optimization is time efficient with computation time that does not increase exponentially with the number of states. This makes it possible to include more

HYBRID SYSTEM BASED FUZZY-PID CONTROL SCHEMES FOR UNPREDICTABLE PROCESS

Directory of Open Access Journals (Sweden)

M.K. Tan

2011-07-01

Full Text Available In general, the primary aim of polymerization industry is to enhance the process operation in order to obtain high quality and purity product. However, a sudden and large amount of heat will be released rapidly during the mixing process of two reactants, i.e. phenol and formalin due to its exothermic behavior. The unpredictable heat will cause deviation of process temperature and hence affect the quality of the product. Therefore, it is vital to control the process temperature during the polymerization. In the modern industry, fuzzy logic is commonly used to auto-tune PID controller to control the process temperature. However, this method needs an experienced operator to fine tune the fuzzy membership function and universe of discourse via trial and error approach. Hence, the setting of fuzzy inference system might not be accurate due to the human errors. Besides that, control of the process can be challenging due to the rapid changes in the plant parameters which will increase the process complexity. This paper proposes an optimization scheme using hybrid of Q-learning (QL and genetic algorithm (GA to optimize the fuzzy membership function in order to allow the conventional fuzzy-PID controller to control the process temperature more effectively. The performances of the proposed optimization scheme are compared with the existing fuzzy-PID scheme. The results show that the proposed optimization scheme is able to control the process temperature more effectively even if disturbance is introduced.

Adaptive Hybrid Control of Vehicle Semiactive Suspension Based on Road Profile Estimation

Directory of Open Access Journals (Sweden)

Yechen Qin

2015-01-01

Full Text Available A new road estimation based suspension hybrid control strategy is proposed. Its aim is to adaptively change control gains to improve both ride comfort and road handling with the constraint of rattle space. To achieve this, analytical expressions for ride comfort, road handling, and rattle space with respect to road input are derived based on the hybrid control, and the problem is transformed into a MOOP (Multiobjective Optimization Problem and has been solved by NSGA-II (Nondominated Sorting Genetic Algorithm-II. A new road estimation and classification method, which is based on ANFIS (Adaptive Neurofuzzy Inference System and wavelet transforms, is then presented as a means of detecting the road profile level, and a Kalman filter is designed for observing unknown states. The results of simulations conducted with random road excitation show that the efficiency of the proposed control strategy compares favourably to that of a passive system.

A Hybrid Metaheuristic-Based Approach for the Aerodynamic Optimization of Small Hybrid Wind Turbine Rotors

DEFF Research Database (Denmark)

Herbert-Acero, José F.; Martínez-Lauranchet, Jaime; Probst, Oliver

2014-01-01

of the sectional blade aerodynamics. The framework considers an innovative nested-hybrid solution procedure based on two metaheuristics, the virtual gene genetic algorithm and the simulated annealing algorithm, to provide a near-optimal solution to the problem. The objective of the study is to maximize...

On the Likely Utility of Hybrid Weights Optimized for Variances in Hybrid Error Covariance Models

Science.gov (United States)

Satterfield, E.; Hodyss, D.; Kuhl, D.; Bishop, C. H.

2017-12-01

Because of imperfections in ensemble data assimilation schemes, one cannot assume that the ensemble covariance is equal to the true error covariance of a forecast. Previous work demonstrated how information about the distribution of true error variances given an ensemble sample variance can be revealed from an archive of (observation-minus-forecast, ensemble-variance) data pairs. Here, we derive a simple and intuitively compelling formula to obtain the mean of this distribution of true error variances given an ensemble sample variance from (observation-minus-forecast, ensemble-variance) data pairs produced by a single run of a data assimilation system. This formula takes the form of a Hybrid weighted average of the climatological forecast error variance and the ensemble sample variance. Here, we test the extent to which these readily obtainable weights can be used to rapidly optimize the covariance weights used in Hybrid data assimilation systems that employ weighted averages of static covariance models and flow-dependent ensemble based covariance models. Univariate data assimilation and multi-variate cycling ensemble data assimilation are considered. In both cases, it is found that our computationally efficient formula gives Hybrid weights that closely approximate the optimal weights found through the simple but computationally expensive process of testing every plausible combination of weights.

Hybrid Control of Long-Endurance Aerial Robotic Vehicles for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Deok-Jin Lee

2011-06-01

Full Text Available This paper presents an effective hybrid control approach for building stable wireless sensor networks between heterogeneous unmanned vehicles using long‐ endurance aerial robotic vehicles. For optimal deployment of the aerial vehicles in communication networks, a gradient climbing based self‐estimating control algorithm is utilized to locate the aerial platforms to maintain maximum communication throughputs between distributed multiple nodes. The autonomous aerial robots, which function as communication relay nodes, extract and harvest thermal energy from the atmospheric environment to improve their flight endurance within specified communication coverage areas. The rapidly‐deployable sensor networks with the high‐endurance aerial vehicles can be used for various application areas including environment monitoring, surveillance, tracking, and decision‐making support. Flight test and simulation studies are conducted to evaluate the effectiveness of the proposed hybrid control technique for robust communication networks.

A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics.

Science.gov (United States)

Gomaa Haroun, A H; Li, Yin-Ya

2017-11-01

In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern. Copyright © 2017 ISA. Published by

Actuator Location and Voltages Optimization for Shape Control of Smart Beams Using Genetic Algorithms

Directory of Open Access Journals (Sweden)

Georgios E. Stavroulakis

2013-10-01

Full Text Available This paper presents a numerical study on optimal voltages and optimal placement of piezoelectric actuators for shape control of beam structures. A finite element model, based on Timoshenko beam theory, is developed to characterize the behavior of the structure and the actuators. This model accounted for the electromechanical coupling in the entire beam structure, due to the fact that the piezoelectric layers are treated as constituent parts of the entire structural system. A hybrid scheme is presented based on great deluge and genetic algorithm. The hybrid algorithm is implemented to calculate the optimal locations and optimal values of voltages, applied to the piezoelectric actuators glued in the structure, which minimize the error between the achieved and the desired shape. Results from numerical simulations demonstrate the capabilities and efficiency of the developed optimization algorithm in both clamped−free and clamped−clamped beam problems are presented.

Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm

Directory of Open Access Journals (Sweden)

Jingxian Hao

2016-11-01

Full Text Available The rule-based logic threshold control strategy has been frequently used in energy management strategies for hybrid electric vehicles (HEVs owing to its convenience in adjusting parameters, real-time performance, stability, and robustness. However, the logic threshold control parameters cannot usually ensure the best vehicle performance at different driving cycles and conditions. For this reason, the optimization of key parameters is important to improve the fuel economy, dynamic performance, and drivability. In principle, this is a multiparameter nonlinear optimization problem. The logic threshold energy management strategy for an all-wheel-drive HEV is comprehensively analyzed and developed in this study. Seven key parameters to be optimized are extracted. The optimization model of key parameters is proposed from the perspective of fuel economy. The global optimization method, DIRECT algorithm, which has good real-time performance, low computational burden, rapid convergence, is selected to optimize the extracted key parameters globally. The results show that with the optimized parameters, the engine operates more at the high efficiency range resulting into a fuel savings of 7% compared with non-optimized parameters. The proposed method can provide guidance for calibrating the parameters of the vehicle energy management strategy from the perspective of fuel economy.

An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization

Directory of Open Access Journals (Sweden)

Lihong Guo

2013-01-01

Full Text Available A hybrid metaheuristic approach by hybridizing harmony search (HS and firefly algorithm (FA, namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.

Hybrid Modeling and Optimization of Yogurt Starter Culture Continuous Fermentation

Directory of Open Access Journals (Sweden)

Silviya Popova

2009-10-01

Full Text Available The present paper presents a hybrid model of yogurt starter mixed culture fermentation. The main nonlinearities within a classical structure of continuous process model are replaced by neural networks. The new hybrid model accounts for the dependence of the two microorganisms' kinetics from the on-line measured characteristics of the culture medium - pH. Then the model was used further for calculation of the optimal time profile of pH. The obtained results are with agreement with the experimental once.

Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach

International Nuclear Information System (INIS)

Meo, Santolo; Zohoori, Alireza; Vahedi, Abolfazl

2016-01-01

Highlights: • A new optimal design of flux switching permanent magnet generator is developed. • A prototype is employed to validate numerical data used for optimization. • A novel hybrid multi-objective particle swarm optimization approach is proposed. • Optimization targets are weight, cost, voltage and its total harmonic distortion. • The hybrid approach preference is proved compared with other optimization methods. - Abstract: In this paper a new hybrid approach obtained combining a multi-objective particle swarm optimization and artificial neural network is proposed for the design optimization of a direct-drive permanent magnet flux switching generators for low power wind applications. The targets of the proposed multi-objective optimization are to reduce the costs and weight of the machine while maximizing the amplitude of the induced voltage as well as minimizing its total harmonic distortion. The permanent magnet width, the stator and rotor tooth width, the rotor teeth number and stator pole number of the machine define the search space for the optimization problem. Four supervised artificial neural networks are designed for modeling the complex relationships among the weight, the cost, the amplitude and the total harmonic distortion of the output voltage respect to the quantities of the search space. Finite element analysis is adopted to generate training dataset for the artificial neural networks. Finite element analysis based model is verified by experimental results with a 1.5 kW permanent magnet flux switching generator prototype suitable for renewable energy applications, having 6/19 stator poles/rotor teeth. Finally the effectiveness of the proposed hybrid procedure is compared with the results given by conventional multi-objective optimization algorithms. The obtained results show the soundness of the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology for optimal design of permanent

Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm

DEFF Research Database (Denmark)

Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar

2017-01-01

India's ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating impact on the utility distribution system brought about by improper setup of these charging...... stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context......, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site...

Fault tolerant control design for hybrid systems

Energy Technology Data Exchange (ETDEWEB)

Yang, Hao; Jiang, Bin [Nanjing University of Aeronautics and Astronautics, Nanjing (China); Cocquempot, Vincent [Universite des Sciences et Technologies de Lille, Villeneuve d' Ascq (France)

2010-07-01

This book intends to provide the readers a good understanding on how to achieve Fault Tolerant Control goal of Hybrid Systems. The book can be used as a reference for the academic research on Fault Tolerant Control and Hybrid Systems or used in Ph.D. study of control theory and engineering. The knowledge background for this monograph would be some undergraduate and graduate courses on Fault Diagnosis and Fault Tolerant Control theory, linear system theory, nonlinear system theory, Hybrid Systems theory and Discrete Event System theory. (orig.)

The conference hybrid control room

International Nuclear Information System (INIS)

Gieci, A.; Caucik, J.; Macko, J.

2008-01-01

An original concept of a hybrid control room was developed for the Mochovce-3 and Mochovce-4 reactor units which are under construction. The basic idea underlying the concept is that the control room should be a main working place for the operators (reactor operator and turbine operator) and for the shift supervisor, designed as a comprehensive unit desk shaped so that all members of the control room crew are in a face-to-face contact constantly. The main desk consists of three clearly identified areas serving the operators and the unit supervisor as their main working places. A soft control system is installed at the main working places. A separate safety-related working place, designed as a panel with classical instrumentations at the conference hybrid control room, is provided in case of abnormal conditions or emergency situation. Principles of ergonomics and cognitive engineering were taken into account when designing the new conference hybrid control room for the Mochovce-3 and -4 reactor units. The sizes, propositions, shapes and disposition of the equipment at the control room have been created and verified by using virtual reality tools. (orig.)

Component sizing optimization of plug-in hybrid electric vehicles

International Nuclear Information System (INIS)

Wu, Xiaolan; Cao, Binggang; Li, Xueyan; Xu, Jun; Ren, Xiaolong

2011-01-01

Plug-in hybrid electric vehicles (PHEVs) are considered as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This paper describes a methodology for the optimization of PHEVs component sizing using parallel chaos optimization algorithm (PCOA). In this approach, the objective function is defined so as to minimize the drivetrain cost. In addition, the driving performance requirements are considered as constraints. Finally, the optimization process is performed over three different all electric range (AER) and two types of batteries. The results from computer simulation show the effectiveness of the approach and the reduction in drivetrian cost while ensuring the vehicle performance.

The phase detection and calculation for low hybrid wave phase-feedback control system

International Nuclear Information System (INIS)

Liu Qiang; Liang Hao; Zhou Yongzhao; Shan Jiafang

2008-01-01

A method of phase detection and calculation for low hybrid wave phase-feedback control system and the implementing the algorithms on DSP cores embedded in FPGA is introduced. By taking the advantages of matlab-aided design and algorithms optimization to carry out parallel processing of multi-channel phase calculation in FPGA with rich resources, the purposed of fast phase-feedback control is achieved under the need of complicated mathematical operations. (authors)

Optimization of an experimental hybrid microgrid operation: reliability and economic issues

OpenAIRE

Milo, Aitor; Gaztañaga, Haizea; Etxeberria Otadui, Ion; Bilbao, Endika; Rodríguez Cortés, Pedro

2009-01-01

In this paper a hybrid microgrid system, composed of RES (Renewable Energy System) and CHP (Combined Heat and Power) systems together with a battery based storage system is presented. The microgrid is accompanied by a centralized energy management system (CEMS) in order to optimize the microgrid operation both in grid-connected and in stand-alone modes. In grid-connected mode the optimization of the economic exploitation of the microgrid is privileged by applying optim...

Optimization of the fission--fusion hybrid concept

International Nuclear Information System (INIS)

Saltmarsh, M.J.; Grimes, W.R.; Santoro, R.T.

1979-04-01

One of the potentially attractive applications of controlled thermonuclear fusion is the fission--fusion hybrid concept. In this report we examine the possible role of the hybrid as a fissile fuel producer. We parameterize the advantages of the concept in terms of the performance of the fusion device and the breeding blanket and discuss some of the more troublesome features of existing design studies. The analysis suggests that hybrids based on deuterium--tritium (D--T) fusion devices are unlikely to be economically attractive and that they present formidable blanket technology problems. We suggest an alternative approach based on a semicatalyzed deuterium--deuterium (D--D) fusion reactor and a molten salt blanket. This concept is shown to emphasize the desirable features of the hybrid, to have considerably greater economic potential, and to mitigate many of the disadvantages of D--T-based systems

Battery control system for hybrid vehicle and method for controlling a hybrid vehicle battery

Science.gov (United States)

Bockelmann, Thomas R [Battle Creek, MI; Beaty, Kevin D [Kalamazoo, MI; Zou, Zhanijang [Battle Creek, MI; Kang, Xiaosong [Battle Creek, MI

2009-07-21

A battery control system for controlling a state of charge of a hybrid vehicle battery includes a detecting arrangement for determining a vehicle operating state or an intended vehicle operating state and a controller for setting a target state of charge level of the battery based on the vehicle operating state or the intended vehicle operating state. The controller is operable to set a target state of charge level at a first level during a mobile vehicle operating state and at a second level during a stationary vehicle operating state or in anticipation of the vehicle operating in the stationary vehicle operating state. The invention further includes a method for controlling a state of charge of a hybrid vehicle battery.

A hybrid pi control scheme for airship hovering

International Nuclear Information System (INIS)

Ashraf, Z.; Choudhry, M.A.; Hanif, A.

2012-01-01

Airship provides us many attractive applications in aerospace industry including transportation of heavy payloads, tourism, emergency management, communication, hover and vision based applications. Hovering control of airship has many utilizations in different engineering fields. However, it is a difficult problem to sustain the hover condition maintaining controllability. So far, different solutions have been proposed in literature but most of them are difficult in analysis and implementation. In this paper, we have presented a simple and efficient scheme to design a multi input multi output hybrid PI control scheme for airship. It can maintain stability of the plant by rejecting disturbance inputs to ensure robustness. A control scheme based on feedback theory is proposed that uses principles of optimality with integral action for hovering applications. Simulations are carried out in MTALAB for examining the proposed control scheme for hovering in different wind conditions. Comparison of the technique with an existing scheme is performed, describing the effectiveness of control scheme. (author)

Load estimator-based hybrid controller design for two-interleaved boost converter dedicated to renewable energy and automotive applications.

Science.gov (United States)

Bougrine, Mohamed; Benmiloud, Mohammed; Benalia, Atallah; Delaleau, Emmanuel; Benbouzid, Mohamed

2017-01-01

This paper is devoted to the development of a hybrid controller for a two-interleaved boost converter dedicated to renewable energy and automotive applications. The control requirements, resumed in fast transient and low input current ripple, are formulated as a problem of fast stabilization of a predefined optimal limit cycle, and solved using hybrid automaton formalism. In addition, a real time estimation of the load is developed using an algebraic approach for online adjustment of the hybrid controller. Mathematical proofs are provided with simulations to illustrate the effectiveness and the robustness of the proposed controller despite different disturbances. Furthermore, a fuel cell system supplying a resistive load through a two-interleaved boost converter is also highlighted. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal design of energy storage systems for hybrid vehicle drivetrains

NARCIS (Netherlands)

Hofman, T.; Hoekstra, D.; Druten, van R.M.; Steinbuch, M.

2005-01-01

Current hybrid powertrain simulation packages arebased on discrete (existing) system components and predefinedsystem structures. Optimization of the performance of the hybridpowertrain is then based on finding the most efficient controlstrategy of the primary and secondary power source and

Hybrid SPECT/CT: Principle, dosimetry and quality control

International Nuclear Information System (INIS)

Hapdey, S.; Gardin, I.; Salles, A.; Rousseliere, F.; Edet-Sanson, A.; Vera, P.

2009-01-01

The recent introduction of hybrid systems combining a SPECT and a CT in nuclear medicine, greatly improved the diagnostic accuracy for particular clinical indications, due to the possible attenuation and/or scatter correction of the SPECT functional images and the availability of helpful anatomic information. Although the gamma cameras performances are noticeably comparable, the associated CT furnished by the manufacturer are relatively different from each other. Whatever the system is, the introduction of CT in the nuclear diagnostic process results in a significant increase of the patient dose. This dose increase should be justified and optimized considering both the clinical question and the CT settings available on these systems. The installation of a hybrid system must be accompanied by the management of a documentary quality insurance program, jointly developed by the technologists, physicists and physicians, both covering its clinical use and the associated dosimetry issues as monitoring its performances. Particular quality control procedures have to be defined because of the coupling between the two devices. (authors)

Adaptive hybrid control of manipulators

Science.gov (United States)

Seraji, H.

1987-01-01

Simple methods for the design of adaptive force and position controllers for robot manipulators within the hybrid control architecuture is presented. The force controller is composed of an adaptive PID feedback controller, an auxiliary signal and a force feedforward term, and it achieves tracking of desired force setpoints in the constraint directions. The position controller consists of adaptive feedback and feedforward controllers and an auxiliary signal, and it accomplishes tracking of desired position trajectories in the free directions. The controllers are capable of compensating for dynamic cross-couplings that exist between the position and force control loops in the hybrid control architecture. The adaptive controllers do not require knowledge of the complex dynamic model or parameter values of the manipulator or the environment. The proposed control schemes are computationally fast and suitable for implementation in on-line control with high sampling rates.

Study on optimal configuration of the grid-connected wind-solar-battery hybrid power system

Science.gov (United States)

Ma, Gang; Xu, Guchao; Ju, Rong; Wu, Tiantian

2017-08-01

The capacity allocation of each energy unit in the grid-connected wind-solar-battery hybrid power system is a significant segment in system design. In this paper, taking power grid dispatching into account, the research priorities are as follows: (1) We establish the mathematic models of each energy unit in the hybrid power system. (2) Based on dispatching of the power grid, energy surplus rate, system energy volatility and total cost, we establish the evaluation system for the wind-solar-battery power system and use a number of different devices as the constraint condition. (3) Based on an improved Genetic algorithm, we put forward a multi-objective optimisation algorithm to solve the optimal configuration problem in the hybrid power system, so we can achieve the high efficiency and economy of the grid-connected hybrid power system. The simulation result shows that the grid-connected wind-solar-battery hybrid power system has a higher comprehensive performance; the method of optimal configuration in this paper is useful and reasonable.

Hybrid particle swarm optimization algorithm and its application in nuclear engineering

International Nuclear Information System (INIS)

Liu, C.Y.; Yan, C.Q.; Wang, J.J.

2014-01-01

Highlights: • We propose a hybrid particle swarm optimization algorithm (HPSO). • Modified Nelder–Mead simplex search method is applied in HPSO. • The algorithm has a high search precision and rapidly calculation speed. • HPSO can be used in the nuclear engineering optimization design problems. - Abstract: A hybrid particle swarm optimization algorithm with a feasibility-based rule for solving constrained optimization problems has been developed in this research. Firstly, the global optimal solution zone can be obtained through particle swarm optimization process, and then the refined search of the global optimal solution will be achieved through the modified Nelder–Mead simplex algorithm. Simulations based on two well-studied benchmark problems demonstrate the proposed algorithm will be an efficient alternative to solving constrained optimization problems. The vertical electrical heating pressurizer is one of the key components in reactor coolant system. The mathematical model of pressurizer has been established in steady state. The optimization design of pressurizer weight has been carried out through HPSO algorithm. The results show the pressurizer weight can be reduced by 16.92%. The thermal efficiencies of conventional PWR nuclear power plants are about 31–35% so far, which are much lower than fossil fueled plants based in a steam cycle as PWR. The thermal equilibrium mathematic model for nuclear power plant secondary loop has been established. An optimization case study has been conducted to improve the efficiency of the nuclear power plant with the proposed algorithm. The results show the thermal efficiency is improved by 0.5%

Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus

Science.gov (United States)

Momoh, James; Chattopadhyay, Deb; Elfayoumy, Mahmoud

1996-01-01

The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the

Non-binary Hybrid LDPC Codes: Structure, Decoding and Optimization

OpenAIRE

Sassatelli, Lucile; Declercq, David

2007-01-01

In this paper, we propose to study and optimize a very general class of LDPC codes whose variable nodes belong to finite sets with different orders. We named this class of codes Hybrid LDPC codes. Although efficient optimization techniques exist for binary LDPC codes and more recently for non-binary LDPC codes, they both exhibit drawbacks due to different reasons. Our goal is to capitalize on the advantages of both families by building codes with binary (or small finite set order) and non-bin...

System-wide hybrid MPC-PID control of a continuous pharmaceutical tablet manufacturing process via direct compaction.

Science.gov (United States)

Singh, Ravendra; Ierapetritou, Marianthi; Ramachandran, Rohit

2013-11-01

The next generation of QbD based pharmaceutical products will be manufactured through continuous processing. This will allow the integration of online/inline monitoring tools, coupled with an efficient advanced model-based feedback control systems, to achieve precise control of process variables, so that the predefined product quality can be achieved consistently. The direct compaction process considered in this study is highly interactive and involves time delays for a number of process variables due to sensor placements, process equipment dimensions, and the flow characteristics of the solid material. A simple feedback regulatory control system (e.g., PI(D)) by itself may not be sufficient to achieve the tight process control that is mandated by regulatory authorities. The process presented herein comprises of coupled dynamics involving slow and fast responses, indicating the requirement of a hybrid control scheme such as a combined MPC-PID control scheme. In this manuscript, an efficient system-wide hybrid control strategy for an integrated continuous pharmaceutical tablet manufacturing process via direct compaction has been designed. The designed control system is a hybrid scheme of MPC-PID control. An effective controller parameter tuning strategy involving an ITAE method coupled with an optimization strategy has been used for tuning of both MPC and PID parameters. The designed hybrid control system has been implemented in a first-principles model-based flowsheet that was simulated in gPROMS (Process System Enterprise). Results demonstrate enhanced performance of critical quality attributes (CQAs) under the hybrid control scheme compared to only PID or MPC control schemes, illustrating the potential of a hybrid control scheme in improving pharmaceutical manufacturing operations. Copyright © 2013 Elsevier B.V. All rights reserved.

Optimization and Optimal Control

CERN Document Server

Chinchuluun, Altannar; Enkhbat, Rentsen; Tseveendorj, Ider

2010-01-01

During the last four decades there has been a remarkable development in optimization and optimal control. Due to its wide variety of applications, many scientists and researchers have paid attention to fields of optimization and optimal control. A huge number of new theoretical, algorithmic, and computational results have been observed in the last few years. This book gives the latest advances, and due to the rapid development of these fields, there are no other recent publications on the same topics. Key features: Provides a collection of selected contributions giving a state-of-the-art accou

Optimal control strategy design for extending all-electric driving capability of plug-in hybrid electric vehicles (PHEVs)

Energy Technology Data Exchange (ETDEWEB)

Williamson, S.S [Concordia Univ., Montreal, PQ (Canada). Dept. of Electrical and Computer Engineering, P.D Ziogas Power Electronics Laboratory

2007-07-01

The high voltage energy storage system in plug-in hybrid electric vehicles (PHEVs) is usually a rechargeable type that service a dual purpose, notably to supplement the power delivered by the internal combustion engine, and to provide partial propulsion energy from an off-board source of electricity. The energy storage devices in electric vehicles typically improve vehicle efficiency through engine downsizing and by recapturing braking energy. However, since PHEVs have the ability to recharge their energy storage systems directly from the power grid, the periods of all-electric operation can be extended, thereby reducing the dependence on the internal combustion engine. This is particularly useful in city driving conditions. Developers of PHEV technology are faced with the challenge of choosing the appropriate energy storage battery in order to improve the all-electric drive range. In this study, control strategies were modeled for specific driving load conditions using the Advanced Vehicle Simulator (ADVISOR) software. This paper presented specific control algorithms for PHEV operation for various city driving loads. The optimal design strategy considered the improvement of critical energy storage parameters, overall drive train efficiency, and vehicle performance characteristics. Future trends in the design and development of PHEV drive trains were also presented. 13 figs.

A Single-Degree-of-Freedom Energy Optimization Strategy for Power-Split Hybrid Electric Vehicles

Directory of Open Access Journals (Sweden)

Chaoying Xia

2017-07-01

Full Text Available This paper presents a single-degree-of-freedom energy optimization strategy to solve the energy management problem existing in power-split hybrid electric vehicles (HEVs. The proposed strategy is based on a quadratic performance index, which is innovatively designed to simultaneously restrict the fluctuation of battery state of charge (SOC and reduce fuel consumption. An extended quadratic optimal control problem is formulated by approximating the fuel consumption rate as a quadratic polynomial of engine power. The approximated optimal control law is obtained by utilizing the solution properties of the Riccati equation and adjoint equation. It is easy to implement in real-time and the engineering significance is explained in details. In order to validate the effectiveness of the proposed strategy, the forward-facing vehicle simulation model is established based on the ADVISOR software (Version 2002, National Renewable Energy Laboratory, Golden, CO, USA. The simulation results show that there is only a little fuel consumption difference between the proposed strategy and the Pontryagin’s minimum principle (PMP-based global optimal strategy, and the proposed strategy also exhibits good adaptability under different initial battery SOC, cargo mass and road slope conditions.

Advanced control approach for hybrid systems based on solid oxide fuel cells

International Nuclear Information System (INIS)

Ferrari, Mario L.

2015-01-01

Highlights: • Advanced new control system for SOFC based hybrid plants. • Proportional–Integral approach with feed-forward technology. • Good control of fuel cell temperature. • All critical properties maintained inside safe conditions. - Abstract: This paper shows a new advanced control approach for operations in hybrid systems equipped with solid oxide fuel cell technology. This new tool, which combines feed-forward and standard proportional–integral techniques, controls the system during load changes avoiding failures and stress conditions detrimental to component life. This approach was selected to combine simplicity and good control performance. Moreover, the new approach presented in this paper eliminates the need for mass flow rate meters and other expensive probes, as usually required for a commercial plant. Compared to previous works, better performance is achieved in controlling fuel cell temperature (maximum gradient significantly lower than 3 K/min), reducing the pressure gap between cathode and anode sides (at least a 30% decrease during transient operations), and generating a higher safe margin (at least a 10% increase) for the Steam-to-Carbon Ratio. This new control system was developed and optimized using a hybrid system transient model implemented, validated and tested within previous works. The plant, comprising the coupling of a tubular solid oxide fuel cell stack with a microturbine, is equipped with a bypass valve able to connect the compressor outlet with the turbine inlet duct for rotational speed control. Following model development and tuning activities, several operative conditions were considered to show the new control system increased performance compared to previous tools (the same hybrid system model was used with the new control approach). Special attention was devoted to electrical load steps and ramps considering significant changes in ambient conditions

Modular supervisory controller for hybrid power systems

Energy Technology Data Exchange (ETDEWEB)

Lemos Pereira, A. de

2000-06-01

The power supply of remote places has been commonly provided by thermal power plants, usually diesel generators. Although hybrid power systems may constitute the most economical solution in many applications their widespread application to the electrification schemes of remote areas still depends on improvements in the issues of design and operation control. The main limitations of the present hybrid power systems technology, which are identified in this work, are related to the control and supervision of the power system. Therefore this thesis focuses on the modularity of supervisory controllers in order to design cost-competitive and reliable hybrid power systems. The modular supervisory controller created in this project is considered an important part of a system design approach that aims to overcome the technical difficulties of the current engineering practice and contribute to open the market of hybrid power systems. The term modular refers to a set of design characteristics that allows the use of basically the same supervisory controller in different projects. The modularization and standardisation of the controller include several issues such as interfacing components, communication protocols, modelling, programming and control strategies. The modularity can reduce the highly specialised system engineering related to the integration of components, operation and control. It can also avoid the high costs for installation, service and maintenance. A modular algorithm for supervisory controllers has been developed (a Matlab program called SuperCon) using an object-oriented design and it has been tested through several simulations using different hybrid system configurations and different control strategies. This thesis presents a complete control system design process which can be used as the basis for the development and implementation of intelligent and autonomous supervisory controllers for hybrid power systems with modular characteristics. (au)

Optimal sizing of grid-independent hybrid photovoltaic–battery power systems for household sector

International Nuclear Information System (INIS)

Bianchi, M.; Branchini, L.; Ferrari, C.; Melino, F.

2014-01-01

Highlights: • A feasibility study on a stand-alone solar–battery power generation system is carried out. • An in-house developed calculation code able to estimate photovoltaic panels behaviour is described. • The feasibility of replacing grid electricity with hybrid system is examined. • Guidelines for optimal photovoltaic design are given. • Guidelines for optimal storage sizing in terms of batteries number and capacity are given. - Abstract: The penetration of renewable sources into the grid, particularly wind and solar, have been increasing in recent years. As a consequence, there have been serious concerns over reliable and safety operation of power systems. One possible solution, to improve grid stability, is to integrate energy storage devices into power system network: storing energy produced in periods of low demand to later use, ensuring full exploitation of intermittent available sources. Focusing on stand-alone photovoltaic (PV) energy system, energy storage is needed with the purpose of ensuring continuous power flow, to minimize or, if anything, to neglect electrical grid supply. A comprehensive study on a hybrid stand-alone photovoltaic power system using two different energy storage technologies has been performed. The study examines the feasibility of replacing electricity provided by the grid with hybrid system to meet household demand. In particular, this paper presents first results for photovoltaic (PV)/battery (B) hybrid configuration. The main objective of this paper is focused on PV/B system, to recommend hybrid system optimal design in terms of PV module number, PV module tilt, number and capacity of batteries to minimize or, if possible, to neglect grid supply. This paper is the early stage of a theoretical and experimental study in which two different hybrid power system configurations will be evaluated and compared: (i) PV/B system and (ii) PV/B/fuel cell (FC) system. The aim of the overall study will be the definition of the

Hybrid Metaheuristic Approach for Nonlocal Optimization of Molecular Systems.

Science.gov (United States)

Dresselhaus, Thomas; Yang, Jack; Kumbhar, Sadhana; Waller, Mark P

2013-04-09

Accurate modeling of molecular systems requires a good knowledge of the structure; therefore, conformation searching/optimization is a routine necessity in computational chemistry. Here we present a hybrid metaheuristic optimization (HMO) algorithm, which combines ant colony optimization (ACO) and particle swarm optimization (PSO) for the optimization of molecular systems. The HMO implementation meta-optimizes the parameters of the ACO algorithm on-the-fly by the coupled PSO algorithm. The ACO parameters were optimized on a set of small difluorinated polyenes where the parameters exhibited small variance as the size of the molecule increased. The HMO algorithm was validated by searching for the closed form of around 100 molecular balances. Compared to the gradient-based optimized molecular balance structures, the HMO algorithm was able to find low-energy conformations with a 87% success rate. Finally, the computational effort for generating low-energy conformation(s) for the phenylalanyl-glycyl-glycine tripeptide was approximately 60 CPU hours with the ACO algorithm, in comparison to 4 CPU years required for an exhaustive brute-force calculation.

Advanced hybrid and electric vehicles system optimization and vehicle integration

CERN Document Server

2016-01-01

This contributed volume contains the results of the research program “Agreement for Hybrid and Electric Vehicles”, funded by the International Energy Agency. The topical focus lies on technology options for the system optimization of hybrid and electric vehicle components and drive train configurations which enhance the energy efficiency of the vehicle. The approach to the topic is genuinely interdisciplinary, covering insights from fields. The target audience primarily comprises researchers and industry experts in the field of automotive engineering, but the book may also be beneficial for graduate students.

Component sizing optimization of plug-in hybrid electric vehicles

Energy Technology Data Exchange (ETDEWEB)

Wu, Xiaolan; Cao, Binggang; Li, Xueyan; Xu, Jun; Ren, Xiaolong [School of Mechanical Engineering, Xi' an Jiaotong University, Xi' an, 710049 (China)

2011-03-15

Plug-in hybrid electric vehicles (PHEVs) are considered as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This paper describes a methodology for the optimization of PHEVs component sizing using parallel chaos optimization algorithm (PCOA). In this approach, the objective function is defined so as to minimize the drivetrain cost. In addition, the driving performance requirements are considered as constraints. Finally, the optimization process is performed over three different all electric range (AER) and two types of batteries. The results from computer simulation show the effectiveness of the approach and the reduction in drivetrian cost while ensuring the vehicle performance. (author)

SVC Planning in Large–scale Power Systems via a Hybrid Optimization Method

DEFF Research Database (Denmark)

Yang, Guang ya; Majumder, Rajat; Xu, Zhao

2009-01-01

The research on allocation of FACTS devices has attracted quite a lot interests from various aspects. In this paper, a hybrid model is proposed to optimise the number, location as well as the parameter settings of static Var compensator (SVC) deployed in large–scale power systems. The model...... utilises the result of vulnerability assessment for determining the candidate locations. A hybrid optimisation method including two stages is proposed to find out the optimal solution of SVC in large– scale planning problem. In the first stage, a conventional genetic algorithm (GA) is exploited to generate...... a candidate solution pool. Then in the second stage, the candidates are presented to a linear planning model to investigate the system optimal loadability, hence the optimal solution for SVC planning can be achieved. The method is presented to IEEE 300–bus system....

Improved hybrid optimization algorithm for 3D protein structure prediction.

Science.gov (United States)

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

Recent Advances on Hybrid Intelligent Systems

CERN Document Server

Melin, Patricia; Kacprzyk, Janusz

2013-01-01

This book presents recent advances on hybrid intelligent systems using soft computing techniques for intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain groups of papers around a similar subject. The first part consists of papers with the main theme of hybrid intelligent systems for control and robotics, which are basically state of the art papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of hybrid intelligent systems for pattern recognition and time series prediction, which are basically papers using nature-inspired techniques, like evolutionary algo...

Optimal Solution for VLSI Physical Design Automation Using Hybrid Genetic Algorithm

Directory of Open Access Journals (Sweden)

I. Hameem Shanavas

2014-01-01

Full Text Available In Optimization of VLSI Physical Design, area minimization and interconnect length minimization is an important objective in physical design automation of very large scale integration chips. The objective of minimizing the area and interconnect length would scale down the size of integrated chips. To meet the above objective, it is necessary to find an optimal solution for physical design components like partitioning, floorplanning, placement, and routing. This work helps to perform the optimization of the benchmark circuits with the above said components of physical design using hierarchical approach of evolutionary algorithms. The goal of minimizing the delay in partitioning, minimizing the silicon area in floorplanning, minimizing the layout area in placement, minimizing the wirelength in routing has indefinite influence on other criteria like power, clock, speed, cost, and so forth. Hybrid evolutionary algorithm is applied on each of its phases to achieve the objective. Because evolutionary algorithm that includes one or many local search steps within its evolutionary cycles to obtain the minimization of area and interconnect length. This approach combines a hierarchical design like genetic algorithm and simulated annealing to attain the objective. This hybrid approach can quickly produce optimal solutions for the popular benchmarks.

The Novel Application of Optimization and Charge Blended Energy Management Control for Component Downsizing within a Plug-in Hybrid Electric Vehicle

Directory of Open Access Journals (Sweden)

Ravi Shankar

2012-11-01

Full Text Available  The adoption of Plug-in Hybrid Electric Vehicles (PHEVs is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%–9% being realizable.

Transfer matrix method for dynamics modeling and independent modal space vibration control design of linear hybrid multibody system

Science.gov (United States)

Rong, Bao; Rui, Xiaoting; Lu, Kun; Tao, Ling; Wang, Guoping; Ni, Xiaojun

2018-05-01

In this paper, an efficient method of dynamics modeling and vibration control design of a linear hybrid multibody system (MS) is studied based on the transfer matrix method. The natural vibration characteristics of a linear hybrid MS are solved by using low-order transfer equations. Then, by constructing the brand-new body dynamics equation, augmented operator and augmented eigenvector, the orthogonality of augmented eigenvector of a linear hybrid MS is satisfied, and its state space model expressed in each independent model space is obtained easily. According to this dynamics model, a robust independent modal space-fuzzy controller is designed for vibration control of a general MS, and the genetic optimization of some critical control parameters of fuzzy tuners is also presented. Two illustrative examples are performed, which results show that this method is computationally efficient and with perfect control performance.

A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

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Paweł Sitek

2016-01-01

Full Text Available This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP and constraint logic programming (CLP, were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems. The ECLiPSe system with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.

Model predictive control of an air suspension system with damping multi-mode switching damper based on hybrid model

Science.gov (United States)

Sun, Xiaoqiang; Yuan, Chaochun; Cai, Yingfeng; Wang, Shaohua; Chen, Long

2017-09-01

This paper presents the hybrid modeling and the model predictive control of an air suspension system with damping multi-mode switching damper. Unlike traditional damper with continuously adjustable damping, in this study, a new damper with four discrete damping modes is applied to vehicle semi-active air suspension. The new damper can achieve different damping modes by just controlling the on-off statuses of two solenoid valves, which makes its damping adjustment more efficient and more reliable. However, since the damping mode switching induces different modes of operation, the air suspension system with the new damper poses challenging hybrid control problem. To model both the continuous/discrete dynamics and the switching between different damping modes, the framework of mixed logical dynamical (MLD) systems is used to establish the system hybrid model. Based on the resulting hybrid dynamical model, the system control problem is recast as a model predictive control (MPC) problem, which allows us to optimize the switching sequences of the damping modes by taking into account the suspension performance requirements. Numerical simulations results demonstrate the efficacy of the proposed control method finally.

Adaptive Inverse Optimal Control for Rehabilitation Robot Systems Using Actor-Critic Algorithm

Directory of Open Access Journals (Sweden)

Fancheng Meng

2014-01-01

Full Text Available The higher goal of rehabilitation robot is to aid a person to achieve a desired functional task (e.g., tracking trajectory based on assisted-as-needed principle. To this goal, a new adaptive inverse optimal hybrid control (AHC combining inverse optimal control and actor-critic learning is proposed. Specifically, an uncertain nonlinear rehabilitation robot model is firstly developed that includes human motor behavior dynamics. Then, based on this model, an open-loop error system is formed; thereafter, an inverse optimal control input is designed to minimize the cost functional and a NN-based actor-critic feedforward signal is responsible for the nonlinear dynamic part contaminated by uncertainties. Finally, the AHC controller is proven (through a Lyapunov-based stability analysis to yield a global uniformly ultimately bounded stability result, and the resulting cost functional is meaningful. Simulation and experiment on rehabilitation robot demonstrate the effectiveness of the proposed control scheme.

Integrated Optimization of Speed Profiles and Power Split for a Tram with Hybrid Energy Storage Systems on a Signalized Route

Directory of Open Access Journals (Sweden)

Zhuang Xiao

2018-02-01

Full Text Available A tram with on-board hybrid energy storage systems based on batteries and supercapacitors is a new option for the urban traffic system. This configuration enables the tram to operate in both catenary zones and catenary-free zones, and the storage of regenerative braking energy for later usage. This paper presents a multiple phases integrated optimization (MPIO method for the coordination of speed profiles and power split considering the signal control strategy. The objective is to minimize the equivalent total energy consumption of all the power sources, which includes both the energy from the traction substation and energy storage systems. The constraints contain running time, variable gradients and curves, speed limits, power balance and signal time at some intersections. The integrated optimization problem is formulated as a multiple phases model based on the characters of the signalized route. An integrated calculation framework, using hp-adaptive pseudospectral method, is proposed for the integrated optimization problem. The effectiveness of the method is verified under fixed time signal (FTS control strategy and tram priority signal (TPS control strategy. Illustrative results show that this method can be successfully applied for trams with hybrid energy storage systems to improve their energy efficiency.

Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches

Directory of Open Access Journals (Sweden)

Jui-Yu Wu

2013-01-01

Full Text Available Stochastic global optimization (SGO algorithms such as the particle swarm optimization (PSO approach have become popular for solving unconstrained global optimization (UGO problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO method and an artificial immune algorithm-based PSO (AIA-PSO method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.

Predictive cruise control in hybrid electric vehicles

NARCIS (Netherlands)

Keulen, T. van; Naus, M.J.G.; Jager, B. de; Molengraft, G.J.L. van de; Steinbuch, M.; Aneke, N.P.I.

2009-01-01

Deceleration rates have considerable influence on the fuel economy of hybrid electric vehicles. Given the vehicle characteristics and actual/measured operating conditions, as well as upcoming route information, optimal velocity trajectories can be constructed that maximize energy recovery. To

Optimization of Hybrid Renewable Energy Systems

Science.gov (United States)

Contreras Cordero, Francisco Jose

Use of diesel generators in remote communities is economically and environmentally unsustainable. Consequently, researchers have focussed on designing hybrid renewable energy systems (HRES) for distributed electricity generation in remote communities. However, the cost-effectiveness of interconnecting multiple remote communities (microgrids) has not been explored. The main objective of this thesis is to develop a methodology for optimal design of HRES and microgrids for remote communities. A set of case studies was developed to test this methodology and it was determined that a combination of stand-alone decentralized HRES and microgrids is the most cost-effective power generation scheme when studying a group of remote communities.

Particle swarm optimization based fuzzy logic controller for autonomous green power energy system with hydrogen storage

International Nuclear Information System (INIS)

Safari, S.; Ardehali, M.M.; Sirizi, M.J.

2013-01-01

Highlights: ► Optimized fuzzy logic controller for a hybrid green power system is developed. ► PSO algorithm is used to optimize membership functions of controller. ► Optimized fuzzy logic controller results in lower O and M costs and LPSP. ► Optimization results in less variation of battery state of charge. - Abstract: The objective of this study is to develop an optimized fuzzy logic controller (FLC) for operating an autonomous hybrid green power system (HGPS) based on the particle swarm optimization (PSO) algorithm. An electrolyzer produces hydrogen from surplus energy generated by the wind turbine and photovoltaic array of HGPS for later use by a fuel cell. The PSO algorithm is used to optimize membership functions of the FLC. The FLC inputs are (a) net power flow and (b) batteries state of charge (SOC) and FLC output determines the time for hydrogen production or consumption. Actual data for weekly residential load, wind speed, ambient temperature, and solar irradiation are used for performance simulation and analysis of the HGPS examined. The weekly operation and maintenance (O and M) costs and the loss of power supply probability (LPSP) are considered in the optimization procedure. It is determined that FLC optimization results in (a) reduced fluctuations in batteries SOC which translates into longer life for batteries and the average SOC is increased by 6.18% and (b) less working hours for fuel cell, when the load is met by wind and PV. It is found that the optimized FLC results in lower O and M costs and LPSP by 57% and 33%, respectively, as compared to its un-optimized counterpart. In addition, a reduction of 18% in investment cost is achievable by optimal sizing and reducing the capacity of HGPS equipment.

Power-optimal force decoupling in a hybrid linear reluctance motor

NARCIS (Netherlands)

Overboom, T.T.; Smeets, J.P.C.; Jansen, J.W.; Lomonova, E.A.; Mavrudieva, D.

2015-01-01

This paper concerns the power-optimal decoupling of the propulsion and normal force created by a hybrid linear reluctance motor. The intrinsic limitations to the decoupling is addressed by the visualizing each force component with a quadric surface in the Euclidean space which is spanned by the

Optimized Controller Design for a 12-Pulse Voltage Source Converter Based HVDC System

Science.gov (United States)

Agarwal, Ruchi; Singh, Sanjeev

2017-12-01

The paper proposes an optimized controller design scheme for power quality improvement in 12-pulse voltage source converter based high voltage direct current system. The proposed scheme is hybrid combination of golden section search and successive linear search method. The paper aims at reduction of current sensor and optimization of controller. The voltage and current controller parameters are selected for optimization due to its impact on power quality. The proposed algorithm for controller optimizes the objective function which is composed of current harmonic distortion, power factor, and DC voltage ripples. The detailed designs and modeling of the complete system are discussed and its simulation is carried out in MATLAB-Simulink environment. The obtained results are presented to demonstrate the effectiveness of the proposed scheme under different transient conditions such as load perturbation, non-linear load condition, voltage sag condition, and tapped load fault under one phase open condition at both points-of-common coupling.

Design and control of a hybrid mount featuring a magnetorheological fluid and a piezostack

International Nuclear Information System (INIS)

Han, Young-Min; Choi, Sang-Min; Choi, Seung-Bok; Lee, Ho-Guen

2011-01-01

In this study, a hybrid mount featuring a magnetorheological (MR) fluid and a piezostack is devised to reduce vibrations occuring in dynamic systems which are operated in a wide frequency range. An MR fluid is adopted to improve isolation performance at resonant low frequencies, whereas a piezostack actuator is adopted for performance improvement at non-resonant high frequencies. As a first step, a passive rubber part is manufactured and its dynamic characteristics are experimentally evaluated. By adopting the MR fluid and the piezostack, semi-active and active actuating mechanisms are devised and their mathematical models are derived. In particular, the magnetic circuit for MR operation is optimally designed via finite element analysis. After evaluating the dynamic characteristics of the manufactured MR device and inertial piezostack actuator, the proposed hybrid mount is then established by integrating them with the rubber part. Subsequently, a vibration control system is constructed using the proposed hybrid mount, and a sliding mode controller (SMC) is designed to attenuate the vibrations transmitted from the base excitation. Control performances of the proposed mount are experimentally evaluated in time and frequency domains

Effect of gear shift and engine start losses on control strategies for hybrid electric vehicles

NARCIS (Netherlands)

Ngo, V.; Hofman, T.; Steinbuch, M.; Serrarens, A.

2012-01-01

In this paper, energetic loss models in the events of shifting gear and starting engine in a parallel Hybrid Electric Vehicle equipped with an Automated Manual Transmission (AMT) will be introduced. The optimal control algorithm for the start-stop, power split and gear shift problem based on Dynamic

OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

Directory of Open Access Journals (Sweden)

Dhananjay Kumar

2016-01-01

Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

Real-Time Performance of Hybrid Mobile Robot Control Utilizing USB Protocol

Directory of Open Access Journals (Sweden)

Jacek Augustyn

2015-02-01

Full Text Available This article discusses the problem of usability of the USB 2.0 protocol in the area of real-time control of a mobile robot. Optimization methods of data transfer handling were proposed. The impact of the optimization results on the entire system's performance was examined in practice. As a test-bed, a hybrid system composed of two devices communicating by direct USB connection was implemented. The first of the mentioned devices was a 32-bit SoC micro-system serving as a direct control unit, and the second one was an off-the-shelf PDA providing supervisory control and logging. Due to this design, the system meets regimes of the real-time constraints and maintains continuity of a data stream at a large bandwidth. The real-time performances of subsystems and the entire system were experimentally examined depending on various operating conditions. Thanks to the performed experiments, the dependency of real-time limits on operational parameters has been determined.

A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources

International Nuclear Information System (INIS)

Kefayat, M.; Lashkar Ara, A.; Nabavi Niaki, S.A.

2015-01-01

Highlights: • A probabilistic optimization framework incorporated with uncertainty is proposed. • A hybrid optimization approach combining ACO and ABC algorithms is proposed. • The problem is to deal with technical, environmental and economical aspects. • A fuzzy interactive approach is incorporated to solve the multi-objective problem. • Several strategies are implemented to compare with literature methods. - Abstract: In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods

Artificial Intelligence Based Control Power Optimization on Tailless Aircraft. [ARMD Seedling Fund Phase I

Science.gov (United States)

Gern, Frank; Vicroy, Dan D.; Mulani, Sameer B.; Chhabra, Rupanshi; Kapania, Rakesh K.; Schetz, Joseph A.; Brown, Derrell; Princen, Norman H.

2014-01-01

Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling large arrays of control devices on innovative air vehicles. Artificial neutral networks are inspired by biological nervous systems and neurocomputing has successfully been applied to a variety of complex optimization problems. This project investigates the potential of applying neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts to minimize control power, hinge moments, and actuator forces, while keeping system weights within acceptable limits. The main objective of this project is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft featuring multiple independently actuated control surfaces. A Nastran aeroservoelastic finite element model is used to generate a learning database of hinge moment and actuation power characteristics for an array of flight conditions and control surface deflections. An artificial neural network incorporating a genetic algorithm then uses this training data to perform control allocation optimization for the investigated aircraft configuration. The phase I project showed that optimization results for the sum of required hinge moments are improved by more than 12% over the best Nastran solution by using the neural network optimization process.

Open Issues in Supervisory Control of Hybrid Electric Vehicles: A Unified Approach Using Optimal Control Methods Questions ouvertes sur la supervision énergétique des véhicules hybrides électriques : une approche unifiée par la théorie de la commande optimale

Directory of Open Access Journals (Sweden)

Serrao L.

2013-03-01

Full Text Available Energy management of hybrid propulsion systems is considered, presenting new issues that extend the energy management role beyond the standard torque splitting to maximize system efficiency. The new issues include additional optimization criteria, constraints and relevant dynamics to deal with. New optimization criteria in addition the sole fuel consumption minimization include engine-out pollutant emissions and battery aging. State constraints are modified to account for plug-in hybrid vehicles. Moreover, specific supervisory control problems are recognized to need additional state variables. The latter comprise: engine and catalyst temperature to deal with engine warm-up effects on fuel consumption and after-catalyst emissions; thermal dynamics of heat recovery systems (Rankine or Thermo-Electric Generators, TEGs; and battery temperature, which influences battery performance and aging. It is shown that all these control problems can be treated in an unified fashion by extending the well-known ECMS (Equivalent Consumption Minimization Strategy, which is an implementation of Pontryagin Minimum Principle (PMP as formulated by optimal control theory. Extended definitions of the Hamiltonian function and Lagrange multipliers are introduced. Optimization runs performed off line are reported. Results show the benefits of the proposed unified approach and enlighten some first online implementation issues. Cet article a pour objet la gestion optimale de l’énergie pour un système de propulsion hybride. Le problème traditionnel de répartition de la puissance est modifié avec des nouveaux objectifs d’optimisation et des nouvelles contraintes. Les nouveaux objectifs d’optimisation incluent les émissions de polluants et le vieillissement de la batterie. Les contraintes sont modifiées pour prendre en compte des batteries à recharge externe (hybrides plug-in. De plus, des problèmes spécifiques sont traités avec une modélisation plus d

A study on optimization of hybrid drive train using Advanced Vehicle Simulator (ADVISOR)

Energy Technology Data Exchange (ETDEWEB)

Same, Adam; Stipe, Alex; Grossman, David; Park, Jae Wan [Department of Mechanical and Aeronautical Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616 (United States)

2010-10-01

This study investigates the advantages and disadvantages of three hybrid drive train configurations: series, parallel, and ''through-the-ground'' parallel. Power flow simulations are conducted with the MATLAB/Simulink-based software ADVISOR. These simulations are then applied in an application for the UC Davis SAE Formula Hybrid vehicle. ADVISOR performs simulation calculations for vehicle position using a combined backward/forward method. These simulations are used to study how efficiency and agility are affected by the motor, fuel converter, and hybrid configuration. Three different vehicle models are developed to optimize the drive train of a vehicle for three stages of the SAE Formula Hybrid competition: autocross, endurance, and acceleration. Input cycles are created based on rough estimates of track geometry. The output from these ADVISOR simulations is a series of plots of velocity profile and energy storage State of Charge that provide a good estimate of how the Formula Hybrid vehicle will perform on the given course. The most noticeable discrepancy between the input cycle and the actual velocity profile of the vehicle occurs during deceleration. A weighted ranking system is developed to organize the simulation results and to determine the best drive train configuration for the Formula Hybrid vehicle. Results show that the through-the-ground parallel configuration with front-mounted motors achieves an optimal balance of efficiency, simplicity, and cost. ADVISOR is proven to be a useful tool for vehicle power train design for the SAE Formula Hybrid competition. This vehicle model based on ADVISOR simulation is applicable to various studies concerning performance and efficiency of hybrid drive trains. (author)

A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system

International Nuclear Information System (INIS)

Al-falahi, Monaaf D.A.; Jayasinghe, S.D.G.; Enshaei, H.

2017-01-01

Highlights: • Possible combinations and configurations for standalone PV-WT HES were discussed. • Most recently used assessment parameters for standalone PV-WT HES were explained. • Optimization algorithms and software tools were comprehensively reviewed. • The recent trend of using hybrid algorithms over single algorithms was discussed. • Optimization algorithms for sizing standalone PV-WT HES were critically compared. - Abstract: Electricity demand in remote and island areas are generally supplied by diesel or other fossil fuel based generation systems. Nevertheless, due to the increasing cost and harmful emissions of fossil fuels there is a growing trend to use standalone hybrid renewable energy systems (HRESs). Due to the complementary characteristics, matured technologies and availability in most areas, hybrid systems with solar and wind energy have become the popular choice in such applications. However, the intermittency and high net present cost are the challenges associated with solar and wind energy systems. In this context, optimal sizing is a key factor to attain a reliable supply at a low cost through these standalone systems. Therefore, there has been a growing interest to develop algorithms for size optimization in standalone HRESs. The optimal sizing methodologies reported so far can be broadly categorized as classical algorithms, modern techniques and software tools. Modern techniques, based on single artificial intelligence (AI) algorithms, are becoming more popular than classical algorithms owing to their capabilities in solving complex optimization problems. Moreover, in recent years, there has been a clear trend to use hybrid algorithms over single algorithms mainly due to their ability to provide more promising optimization results. This paper aims to present a comprehensive review on recent developments in size optimization methodologies, as well as a critical comparison of single algorithms, hybrid algorithms, and software tools

Joint Optimization of Star P-hub Median Problem and Seat Inventory Control Decisions Considering a Hybrid Routing Transportation System

Directory of Open Access Journals (Sweden)

Hamid Tikani

2016-11-01

Full Text Available In this paper, we study the problem of integrated capacitated hub location problem and seat inventory control considering concept and techniques of revenue management. We consider an airline company maximizes its revenue by utilizing the best network topology and providing proper booking limits for all itineraries and fare classes. The transportation system arises in the form of a star/star network and includes both hub-stop and non-stop flights. This problem is formulated as a two-stage stochastic integer program with mixed-integer recourse. We solve various instances carried out from the Turkish network data set. Due to the NP-hardness of the problem, we propose a hybrid optimization method, consisting of an evolutionary algorithm based on genetic algorithm and exact solution. The quality of the solutions found by the proposed meta-heuristic is compared with the original version of GA and the mathematical programming model. The results obtained by the proposed model imply that integrating hub location and seat inventory control problem would help to increase the total revenue of airline companies. Also, in the case of serving non-stop flights, the model can provide more profit by employing less number of hubs.

An optimized hybrid Convolutional Perfectly Matched Layer for efficient absorption of electromagnetic waves

Science.gov (United States)

Darvish, Amirashkan; Zakeri, Bijan; Radkani, Nafiseh

2018-03-01

A hybrid technique is studied in order to improve the performance of Convolutional Perfectly Matched Layer (CPML) in the Finite Difference Time Domain (FDTD) medium. This technique combines the first order of Higdon's annihilation equation as Absorbing Boundary Condition (ABC) with CPML to vanish the Perfect Electric Conductor (PEC) effects at the end of the CPML region. An optimization algorithm is required to find optimum parameters of the proposed absorber. In this investigation, the Particle Swarm Optimization (PSO) is utilized with two separate objective functions in order to minimize the average and peak value of relative error. Using a standard test, the overall performance of the proposed absorber is compared to the original CPML. The results clearly illustrate this method provides approximately 10 dB enhancements in CPML absorption error. The performance, memory and time requirement of the novel absorber, hybrid CPML (H-CPML), was analyzed during 2D and 3D tests and compared to most reported PMLs. The H-CPML requirement of computer resources is similar to CPML and can simply be implemented to truncate FDTD domains. Furthermore, an optimized set of parameters are provided to generalize the hybrid method.

A novel hybrid particle swarm optimization for economic dispatch with valve-point loading effects

Energy Technology Data Exchange (ETDEWEB)

Niknam, Taher, E-mail: niknam@sutech.ac.i [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of); Mojarrad, Hasan Doagou, E-mail: hasan_doagou@yahoo.co [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of); Meymand, Hamed Zeinoddini, E-mail: h.zeinaddini@gmail.co [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of)

2011-04-15

Economic dispatch (ED) is one of the important problems in the operation and management of the electric power systems which is formulated as an optimization problem. Modern heuristics stochastic optimization techniques appear to be efficient in solving ED problem without any restriction because of their ability to seek the global optimal solution. One of modern heuristic algorithms is particle swarm optimization (PSO). In PSO algorithm, particles change place to get close to the best position and find the global minimum point. Also, differential evolution (DE) is a robust statistical method for solving non-linear and non-convex optimization problem. The fast convergence of DE degrades its performance and reduces its search capability that leads to a higher probability towards obtaining a local optimum. In order to overcome this drawback a hybrid method is presented to solve the ED problem with valve-point loading effect by integrating the variable DE with the fuzzy adaptive PSO called FAPSO-VDE. DE is the main optimizer and the PSO is used to maintain the population diversity and prevent leading to misleading local optima for every improvement in the solution of the DE run. The parameters of proposed hybrid algorithm such as inertia weight, mutation and crossover factors are adaptively adjusted. The feasibility and effectiveness of the proposed hybrid algorithm is demonstrated for two case studies and results are compared with those of other methods. It is shown that FAPSO-VDE has high quality solution, superior convergence characteristics and shorter computation time.

Techno–economic design of hybrid electric vehicles and possibilities of the multi-objective optimization structure

International Nuclear Information System (INIS)

Dimitrova, Zlatina; Maréchal, François

2016-01-01

Highlights: • The full hybrid electric vehicle suits for sustainable urban mobility and customer investment. • The full hybrid electric urban vehicle is efficient, with consumption less than 2 L/100 km. • The range extender vehicle is a technology for low CO_2 emissions – less than 20 g/km CO_2_. • The total CO_2 emissions for range extender and plug-in vehicles are sensitive to the use place. - Abstract: The design criteria for modern sustainable development of vehicle powertrain are the high energy efficiency of the conversion system, the competitive cost and the lowest possible environmental impacts. In this article a multi-objective optimization methodology is applied on hybrid electric vehicles study in order to define the optimal powertrain configurations of the vehicle, estimate the cost of the powertrain equipment and show the environmental impact of the technical choices on the lifecycle perspective of the vehicle. The study illustrates optimal design solutions for low fuel consumption vehicles – between 2 L/100 km and 3 L/100 km. For that a simulation model of a hybrid electric vehicle is made. This model is coupled with a cost model for the vehicle. The techno–economic optimizations are performed for two case studies, illustrating the possibilities of the optimization superstructure. Firstly the life cycle inventory is written as a function of the parameters of the techno–economic model. In this way, the obtained environmental indicators from the life cycle assessment are calculated as a function of the decision variables for the vehicle design. In the second example the parameters of the energy distribution function are included as decision variables in the techno–economic optimization and are simultaneously optimized.

Hybrid dynamical systems observation and control

CERN Document Server

Defoort, Michael

2015-01-01

This book is a collection of contributions defining the state of current knowledge and new trends in hybrid systems – systems involving both continuous dynamics and discrete events – as described by the work of several well-known groups of researchers. Hybrid Dynamical Systems presents theoretical advances in such areas as diagnosability, observability and stabilization for various classes of system. Continuous and discrete state estimation and self-triggering control of nonlinear systems are advanced. The text employs various methods, among them, high-order sliding modes, Takagi–Sugeno representation and sampled-data switching to achieve its ends. The many applications of hybrid systems from power converters to computer science are not forgotten; studies of flexible-joint robotic arms and – as representative biological systems – the behaviour of the human heart and vasculature, demonstrate the wide-ranging practical significance of control in hybrid systems. The cross-disciplinary origins of study ...

A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem.

Science.gov (United States)

Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah

2016-01-01

The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.

A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem

Science.gov (United States)

Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah

2016-01-01

The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them. PMID:26819585

A hybrid condenser model for real-time applications in performance monitoring, control and optimization

International Nuclear Information System (INIS)

Ding Xudong; Cai Wenjian; Jia Lei; Wen Changyun; Zhang Guiqing

2009-01-01

In this paper, a simple, yet accurate hybrid modeling technique for condensers is presented. The method starts with fundamental physical principles but captures only few key operational characteristic parameters to predict the system performances. The advantages of the methods lie that linear or non-linear least-squares methods can be directly used to determine no more than four key operational characteristic parameters in the model, which can significantly reduce the computational burden. The developed model is verified with the experimental data taken from a pilot system. The testing results confirm that the proposed model can predict accurately the performance of the real-time operating condenser with the maximum error of less than ±10%. The model technique proposed will have wide applications not only in condenser operating optimization, but also in performance assessment and fault detection and diagnosis.

A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

Energy Technology Data Exchange (ETDEWEB)

Tumuluru, Jaya Shankar [Idaho National Laboratory; McCulloch, Richard Chet James [Idaho National Laboratory

2015-07-01

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

Comments On Clock Models In Hybrid Automata And Hybrid Control Systems

Directory of Open Access Journals (Sweden)

Virginia Ecaterina OLTEAN

2001-12-01

Full Text Available Hybrid systems have received a lot of attention in the past decade and a number of different models have been proposed in order to establish mathematical framework that is able to handle both continuous and discrete aspects. This contribution is focused on two models: hybrid automata and hybrid control systems with continuous-discrete interface and the importance of clock models is emphasized. Simple and relevant examples, some taken from the literature, accompany the presentation.

Optimal integration of a hybrid solar-battery power source into smart home nanogrid with plug-in electric vehicle

Science.gov (United States)

Wu, Xiaohua; Hu, Xiaosong; Teng, Yanqiong; Qian, Shide; Cheng, Rui

2017-09-01

Hybrid solar-battery power source is essential in the nexus of plug-in electric vehicle (PEV), renewables, and smart building. This paper devises an optimization framework for efficient energy management and components sizing of a single smart home with home battery, PEV, and potovoltatic (PV) arrays. We seek to maximize the home economy, while satisfying home power demand and PEV driving. Based on the structure and system models of the smart home nanogrid, a convex programming (CP) problem is formulated to rapidly and efficiently optimize both the control decision and parameters of the home battery energy storage system (BESS). Considering different time horizons of optimization, home BESS prices, types and control modes of PEVs, the parameters of home BESS and electric cost are systematically investigated. Based on the developed CP control law in home to vehicle (H2V) mode and vehicle to home (V2H) mode, the home with BESS does not buy electric energy from the grid during the electric price's peak periods.

Dynamic modeling, experimental evaluation, optimal design and control of integrated fuel cell system and hybrid energy systems for building demands

Science.gov (United States)

Nguyen, Gia Luong Huu

obtained experimental data, the research studied the control of airflow to regulate the temperature of reactors within the fuel processor. The dynamic model provided a platform to test the dynamic response for different control gains. With sufficient sensing and appropriate control, a rapid response to maintain the temperature of the reactor despite an increase in power was possible. The third part of the research studied the use of a fuel cell in conjunction with photovoltaic panels, and energy storage to provide electricity for buildings. This research developed an optimization framework to determine the size of each device in the hybrid energy system to satisfy the electrical demands of buildings and yield the lowest cost. The advantage of having the fuel cell with photovoltaic and energy storage was the ability to operate the fuel cell at baseload at night, thus reducing the need for large battery systems to shift the solar power produced in the day to the night. In addition, the dispatchability of the fuel cell provided an extra degree of freedom necessary for unforeseen disturbances. An operation framework based on model predictive control showed that the method is suitable for optimizing the dispatch of the hybrid energy system.

A Hybrid Approach to the Optimization of Multiechelon Systems

Directory of Open Access Journals (Sweden)

Paweł Sitek

2015-01-01

Full Text Available In freight transportation there are two main distribution strategies: direct shipping and multiechelon distribution. In the direct shipping, vehicles, starting from a depot, bring their freight directly to the destination, while in the multiechelon systems, freight is delivered from the depot to the customers through an intermediate points. Multiechelon systems are particularly useful for logistic issues in a competitive environment. The paper presents a concept and application of a hybrid approach to modeling and optimization of the Multi-Echelon Capacitated Vehicle Routing Problem. Two ways of mathematical programming (MP and constraint logic programming (CLP are integrated in one environment. The strengths of MP and CLP in which constraints are treated in a different way and different methods are implemented and combined to use the strengths of both. The proposed approach is particularly important for the discrete decision models with an objective function and many discrete decision variables added up in multiple constraints. An implementation of hybrid approach in the ECLiPSe system using Eplex library is presented. The Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP and its variants are shown as an illustrative example of the hybrid approach. The presented hybrid approach will be compared with classical mathematical programming on the same benchmark data sets.

Mixed H∞ and passive control for linear switched systems via hybrid control approach

Science.gov (United States)

Zheng, Qunxian; Ling, Youzhu; Wei, Lisheng; Zhang, Hongbin

2018-03-01

This paper investigates the mixed H∞ and passive control problem for linear switched systems based on a hybrid control strategy. To solve this problem, first, a new performance index is proposed. This performance index can be viewed as the mixed weighted H∞ and passivity performance. Then, the hybrid controllers are used to stabilise the switched systems. The hybrid controllers consist of dynamic output-feedback controllers for every subsystem and state updating controllers at the switching instant. The design of state updating controllers not only depends on the pre-switching subsystem and the post-switching subsystem, but also depends on the measurable output signal. The hybrid controllers proposed in this paper can include some existing ones as special cases. Combine the multiple Lyapunov functions approach with the average dwell time technique, new sufficient conditions are obtained. Under the new conditions, the closed-loop linear switched systems are globally uniformly asymptotically stable with a mixed H∞ and passivity performance index. Moreover, the desired hybrid controllers can be constructed by solving a set of linear matrix inequalities. Finally, a numerical example and a practical example are given.

Modeling, hybridization, and optimal charging of electrical energy storage systems

Science.gov (United States)

Parvini, Yasha

The rising rate of global energy demand alongside the dwindling fossil fuel resources has motivated research for alternative and sustainable solutions. Within this area of research, electrical energy storage systems are pivotal in applications including electrified vehicles, renewable power generation, and electronic devices. The approach of this dissertation is to elucidate the bottlenecks of integrating supercapacitors and batteries in energy systems and propose solutions by the means of modeling, control, and experimental techniques. In the first step, the supercapacitor cell is modeled in order to gain fundamental understanding of its electrical and thermal dynamics. The dependence of electrical parameters on state of charge (SOC), current direction and magnitude (20-200 A), and temperatures ranging from -40°C to 60°C was embedded in this computationally efficient model. The coupled electro-thermal model was parameterized using specifically designed temporal experiments and then validated by the application of real world duty cycles. Driving range is one of the major challenges of electric vehicles compared to combustion vehicles. In order to shed light on the benefits of hybridizing a lead-acid driven electric vehicle via supercapacitors, a model was parameterized for the lead-acid battery and combined with the model already developed for the supercapacitor, to build the hybrid battery-supercapacitor model. A hardware in the loop (HIL) setup consisting of a custom built DC/DC converter, micro-controller (muC) to implement the power management strategy, 12V lead-acid battery, and a 16.2V supercapacitor module was built to perform the validation experiments. Charging electrical energy storage systems in an efficient and quick manner, motivated to solve an optimal control problem with the objective of maximizing the charging efficiency for supercapacitors, lead-acid, and lithium ion batteries. Pontryagins minimum principle was used to solve the problems

Proton Conductive Channel Optimization in Methanol Resistive Hybrid Hyperbranched Polyamide Proton Exchange Membrane

Directory of Open Access Journals (Sweden)

Liying Ma

2017-12-01

Full Text Available Based on a previously developed polyamide proton conductive macromolecule, the nano-scale structure of the self-assembled proton conductive channels (PCCs is adjusted via enlarging the nano-scale pore size within the macromolecules. Hyperbranched polyamide macromolecules with different size are synthesized from different monomers to tune the nano-scale pore size within the macromolecules, and a series of hybrid membranes are prepared from these two micromoles to optimize the PCC structure in the proton exchange membrane. The optimized membrane exhibits methanol permeability low to 2.2 × 10−7 cm2/s, while the proton conductivity of the hybrid membrane can reach 0.25 S/cm at 80 °C, which was much higher than the value of the Nafion 117 membrane (0.192 S/cm. By considering the mechanical, dimensional, and the thermal properties, the hybrid hyperbranched polyamide proton exchange membrane (PEM exhibits promising application potential in direct methanol fuel cells (DMFC.

Topology optimization of bounded acoustic problems using the hybrid finite element-wave based method

DEFF Research Database (Denmark)

Goo, Seongyeol; Wang, Semyung; Kook, Junghwan

2017-01-01

This paper presents an alternative topology optimization method for bounded acoustic problems that uses the hybrid finite element-wave based method (FE-WBM). The conventional method for the topology optimization of bounded acoustic problems is based on the finite element method (FEM), which...

Decentralized Feedback Controllers for Exponential Stabilization of Hybrid Periodic Orbits: Application to Robotic Walking*

Science.gov (United States)

Hamed, Kaveh Akbari; Gregg, Robert D.

2016-01-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially stabilize periodic orbits for a class of hybrid dynamical systems arising from bipedal walking. The algorithm assumes a class of parameterized and nonlinear decentralized feedback controllers which coordinate lower-dimensional hybrid subsystems based on a common phasing variable. The exponential stabilization problem is translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities, which can be easily solved with available software packages. A set of sufficient conditions for the convergence of the iterative algorithm to a stabilizing decentralized feedback control solution is presented. The power of the algorithm is demonstrated by designing a set of local nonlinear controllers that cooperatively produce stable walking for a 3D autonomous biped with 9 degrees of freedom, 3 degrees of underactuation, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg. PMID:27990059

Joint cost of energy under an optimal economic policy of hybrid power systems subject to uncertainty

International Nuclear Information System (INIS)

Díaz, Guzmán; Planas, Estefanía; Andreu, Jon; Kortabarria, Iñigo

2015-01-01

Economical optimization of hybrid systems is usually performed by means of LCoE (levelized cost of energy) calculation. Previous works deal with the LCoE calculation of the whole hybrid system disregarding an important issue: the stochastic component of the system units must be jointly considered. This paper deals with this issue and proposes a new fast optimal policy that properly calculates the LCoE of a hybrid system and finds the lowest LCoE. This proposed policy also considers the implied competition among power sources when variability of gas and electricity prices are taken into account. Additionally, it presents a comparative between the LCoE of the hybrid system and its individual technologies of generation by means of a fast and robust algorithm based on vector logical computation. Numerical case analyses based on realistic data are presented that valuate the contribution of technologies in a hybrid power system to the joint LCoE. - Highlights: • We perform the LCoE calculation with the stochastic component jointly considered. • We propose a fast an optimal policy that minimizes the LCoE. • We compare the obtained LCoEs by means of a fast and robust algorithm. • We take into account the competition among gas prices and electricity prices

Optimal and Modular Configuration of Wind Integrated Hybrid Power Plants for Off-Grid Systems

DEFF Research Database (Denmark)

Petersen, Lennart; Iov, Florin; Tarnowski, German Claudio

2018-01-01

This paper focusses on the system configuration of offgrid hybrid power plants including wind power generation. First, a modular and scalable system topology is proposed. Secondly, an optimal sizing algorithm is developed in order to determine the installed capacities of wind turbines, PV system......, battery energy storage system and generator sets. The novelty of this work lies in a robust sizing algorithm with respect to the required resolution of resource data in order to account for intra-hour power variations. Moreover, the involvement of the electrical infrastructure enables a precise estimation...... of power losses within the hybrid power plant as well as the consideration of both active and reactive power load demand for optimally sizing the plant components. The main outcome of this study is a methodology to determine feasible system configurations of modular and scalable wind integrated hybrid...

Optimization of ultrasonic array inspections using an efficient hybrid model and real crack shapes

Energy Technology Data Exchange (ETDEWEB)

Felice, Maria V., E-mail: maria.felice@bristol.ac.uk [Department of Mechanical Engineering, University of Bristol, Bristol, U.K. and NDE Laboratory, Rolls-Royce plc., Bristol (United Kingdom); Velichko, Alexander, E-mail: p.wilcox@bristol.ac.uk; Wilcox, Paul D., E-mail: p.wilcox@bristol.ac.uk [Department of Mechanical Engineering, University of Bristol, Bristol (United Kingdom); Barden, Tim; Dunhill, Tony [NDE Laboratory, Rolls-Royce plc., Bristol (United Kingdom)

2015-03-31

Models which simulate the interaction of ultrasound with cracks can be used to optimize ultrasonic array inspections, but this approach can be time-consuming. To overcome this issue an efficient hybrid model is implemented which includes a finite element method that requires only a single layer of elements around the crack shape. Scattering Matrices are used to capture the scattering behavior of the individual cracks and a discussion on the angular degrees of freedom of elastodynamic scatterers is included. Real crack shapes are obtained from X-ray Computed Tomography images of cracked parts and these shapes are inputted into the hybrid model. The effect of using real crack shapes instead of straight notch shapes is demonstrated. An array optimization methodology which incorporates the hybrid model, an approximate single-scattering relative noise model and the real crack shapes is then described.

Nuclear hybrid energy infrastructure

Energy Technology Data Exchange (ETDEWEB)

Agarwal, Vivek; Tawfik, Magdy S.

2015-02-01

The nuclear hybrid energy concept is becoming a reality for the US energy infrastructure where combinations of the various potential energy sources (nuclear, wind, solar, biomass, and so on) are integrated in a hybrid energy system. This paper focuses on challenges facing a hybrid system with a Small Modular Reactor at its core. The core of the paper will discuss efforts required to develop supervisory control center that collects data, supports decision-making, and serves as an information hub for supervisory control center. Such a center will also be a model for integrating future technologies and controls. In addition, advanced operations research, thermal cycle analysis, energy conversion analysis, control engineering, and human factors engineering will be part of the supervisory control center. Nuclear hybrid energy infrastructure would allow operators to optimize the cost of energy production by providing appropriate means of integrating different energy sources. The data needs to be stored, processed, analyzed, trended, and projected at right time to right operator to integrate different energy sources.

Novel hybrid coatings with controlled wettability by composite nanoparticle aggregation

Energy Technology Data Exchange (ETDEWEB)

Hritcu, Doina, E-mail: dhritcu@ch.tuiasi.ro; Dodi, Gianina; Iordache, Mirabela L.; Draganescu, Dan; Sava, Elena; Popa, Marcel I.

2016-11-30

Highlights: • Magnetite-grafted chitosan composite nanoparticles were synthesized. • The particles are able to assemble under the influence of a silane derivative. • Thin films containing composites, chitosan and hydrolyzed silane were optimized. • The novel hybrid coatings show hierarchical roughness and high wetting angle. - Abstract: The aim of this study is to evaluate novel hybrid materials as potential candidates for producing coatings with hierarchical roughness and controlled wetting behaviour. Magnetite (Fe{sub 3}O{sub 4}) nanoparticles obtained by co-precipitation were embedded in matrices synthesized by radical graft co-polymerization of butyl acrylate (BA), butyl methacrylate (BMA), hexyl acrylate (HA) or styrene (ST) with ethylene glycol di-methacrylate (EGDMA) onto previously modified chitosan bearing surface vinyl groups. The resulting composite particles were characterized regarding their average size, composition and magnetic properties. Hybrid thin films containing suspension of composite particles in ethanol and pre-hydrolysed hexadecyltrimethoxysilane (HDTS) as a coupling/crosslinking agent were deposited by spin coating or spraying. The films were cured by heating and subsequently characterized regarding their morphology (scanning electron microscopy), contact angle with water and adhesion to substrate (scratch test). The structure-property relationship is discussed.

Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler

Directory of Open Access Journals (Sweden)

Zhenhao Tang

2017-01-01

Full Text Available To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.

Price-based Optimal Control of Electrical Power Systems

Energy Technology Data Exchange (ETDEWEB)

Jokic, A.

2007-09-10

The research presented in this thesis is motivated by the following issue of concern for the operation of future power systems: Future power systems will be characterized by significantly increased uncertainties at all time scales and, consequently, their behavior in time will be difficult to predict. In Chapter 2 we will present a novel explicit, dynamic, distributed feedback control scheme that utilizes nodal-prices for real-time optimal power balance and network congestion control. The term explicit means that the controller is not based on solving an optimization problem on-line. Instead, the nodal prices updates are based on simple, explicitly defined and easily comprehensible rules. We prove that the developed control scheme, which acts on the measurements from the current state of the system, always provide the correct nodal prices. In Chapter 3 we will develop a novel, robust, hybrid MPC control (model predictive controller) scheme for power balance control with hard constraints on line power flows and network frequency deviations. The developed MPC controller acts in parallel with the explicit controller from Chapter 2, and its task is to enforce the constraints during the transient periods following suddenly occurring power imbalances in the system. In Chapter 4 the concept of autonomous power networks will be presented as a concise formulation to deal with economic, technical and reliability issues in power systems with a large penetration of distributed generating units. With autonomous power networks as new market entities, we propose a novel operational structure of ancillary service markets. In Chapter 5 we will consider the problem of controlling a general linear time-invariant dynamical system to an economically optimal operating point, which is defined by a multiparametric constrained convex optimization problem related with the steady-state operation of the system. The parameters in the optimization problem are values of the exogenous inputs to

Optimizing the current ramp-up phase for the hybrid ITER scenario

International Nuclear Information System (INIS)

Hogeweij, G.M.D.; Citrin, J.; Artaud, J.-F.; Imbeaux, F.; Litaudon, X.; Casper, T.A.; Köchl, F.; Voitsekhovitch, I.

2013-01-01

The current ramp-up phase for the ITER hybrid scenario is analysed with the CRONOS integrated modelling suite. The simulations presented in this paper show that the heating systems available at ITER allow, within the operational limits, the attainment of a hybrid q profile at the end of the current ramp-up. A reference ramp-up scenario is reached by a combination of NBI, ECCD (UPL) and LHCD. A heating scheme with only NBI and ECCD can also reach the target q profile; however, LHCD can play a crucial role in reducing the flux consumption during the ramp-up phase. The optimum heating scheme depends on the chosen transport model, and on assumptions of parameters like n e peaking, edge T e,i and Z eff . The sensitivity of the current diffusion on parameters that are not easily controlled, shows that development of real-time control is important to reach the target q profile. A first step in that direction has been indicated in this paper. Minimizing resistive flux consumption and optimizing the q profile turn out to be conflicting requirements. A trade-off between these two requirements has to be made. In this paper it is shown that fast current ramp with L-mode current overshoot is at the one extreme, i.e. the optimum q profile at the cost of increased resistive flux consumption, whereas early H-mode transition is at the other extreme. (paper)

Evolutionary design of discrete controllers for hybrid mechatronic systems

DEFF Research Database (Denmark)

Dupuis, Jean-Francois; Fan, Zhun; Goodman, Erik

2015-01-01

This paper investigates the issue of evolutionary design of controllers for hybrid mechatronic systems. Finite State Automaton (FSA) is selected as the representation for a discrete controller due to its interpretability, fast execution speed and natural extension to a statechart, which is very...... popular in industrial applications. A case study of a two-tank system is used to demonstrate that the proposed evolutionary approach can lead to a successful design of an FSA controller for the hybrid mechatronic system, represented by a hybrid bond graph. Generalisation of the evolved FSA controller...... of the evolutionary design of controllers for hybrid mechatronic systems. Finally, some important future research directions are pointed out, leading to the major work of the succeeding part of the research....

Hybrid Robust Control Law with Disturbance Observer for High-Frequency Response Electro-Hydraulic Servo Loading System

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Zhiqing Sheng

2016-04-01

Full Text Available Addressing the simulating issue of the helicopter-manipulating booster aerodynamic load with high-frequency dynamic load superimposed on a large static load, this paper studies the design of the robust controller for the electro-hydraulic loading system to realize the simulation of this kind of load. Firstly, the equivalent linear model of the electro-hydraulic loading system under assumed parameter uncertainty is established. Then, a hybrid control scheme is proposed for the loading system. This control scheme consists of a constant velocity feed-forward compensator, a robust inner loop compensator based on disturbance observer and a robust outer loop feedback controller. The constant velocity compensator eliminates most of the extraneous force at first, and then the double-loop cascade composition control strategy is employed to design the compensated system. The disturbance observer–based inner loop compensator further restrains the disturbances including the remaining extraneous force, and makes the actual plant tracking a nominal model approximately in a certain frequency range. The robust outer loop controller achieves the desired force-tracking performance, and guarantees system robustness in the high frequency region. The optimized low-pass filter Q(s is designed by using the H∞ mixed sensitivity optimization method. The simulation results show that the proposed hybrid control scheme and controller can effectively suppress the extraneous force and improve the robustness of the electro-hydraulic loading system.

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

Science.gov (United States)

Choi, D J; Park, H

2001-11-01

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

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Kiran Teeparthi

2017-04-01

Full Text Available In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators.

Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle

International Nuclear Information System (INIS)

Shen, Peihong; Zhao, Zhiguo; Zhan, Xiaowen; Li, Jingwei

2017-01-01

In this paper, an energy management strategy based on logic threshold is proposed for a plug-in hybrid electric vehicle. The plug-in hybrid electric vehicle powertrain model is established using MATLAB/Simulink based on experimental tests of the power components, which is validated by the comparison with the verified simulation model which is built in the AVL Cruise. The influence of the driving torque demand decision on the fuel economy of plug-in hybrid electric vehicle is studied using a simulation. The optimization method for the driving torque demand decision, which refers to the relationship between the accelerator pedal opening and driving torque demand, from the perspective of fuel economy is formulated. The dynamically changing inertia weight particle swarm optimization is used to optimize the decision parameters. The simulation results show that the optimized driving torque demand decision can improve the PHEV fuel economy by 15.8% and 14.5% in the fuel economy test driving cycle of new European driving cycle and worldwide harmonized light vehicles test respectively, using the same rule-based energy management strategy. The proposed optimization method provides a theoretical guide for calibrating the parameters of driving torque demand decision to improve the fuel economy of the real plug-in hybrid electric vehicle. - Highlights: • The influence of the driving torque demand decision on the fuel economy is studied. • The optimization method for the driving torque demand decision is formulated. • An improved particle swarm optimization is utilized to optimize the parameters. • Fuel economy is improved by using the optimized driving torque demand decision.

Soft Computing Optimizer For Intelligent Control Systems Design: The Structure And Applications

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Sergey A. Panfilov

2003-10-01

Full Text Available Soft Computing Optimizer (SCO as a new software tool for design of robust intelligent control systems is described. It is based on the hybrid methodology of soft computing and stochastic simulation. It uses as an input the measured or simulated data about the modeled system. SCO is used to design an optimal fuzzy inference system, which approximates a random behavior of control object with the certain accuracy. The task of the fuzzy inference system construction is reduced to the subtasks such as forming of the linguistic variables for each input and output variable, creation of rule data base, optimization of rule data base and refinement of the parameters of the membership functions. Each task by the corresponding genetic algorithm (with an appropriate fitness function is solved. The result of SCO application is the design of Knowledge Base of a Fuzzy Controller, which contains the value information about developed fuzzy inference system. Such value information can be downloaded into the actual fuzzy controller to perform online fuzzy control. Simulations results of robust fuzzy control of nonlinear dynamic systems and experimental results of application on automotive semi-active suspension control are demonstrated.

Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

DEFF Research Database (Denmark)

Wang, Yong; Cai, Zixing; Zhou, Yuren

2009-01-01

A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...

Control of DNA hybridization by photoswitchable molecular glue.

Science.gov (United States)

Dohno, Chikara; Nakatani, Kazuhiko

2011-12-01

Hybridization of DNA is one of the most intriguing events in molecular recognition and is essential for living matter to inherit life beyond generations. In addition to the function of DNA as genetic material, DNA hybridization is a key to control the function of DNA-based materials in nanoscience. Since the hybridization of two single stranded DNAs is a thermodynamically favorable process, dissociation of the once formed DNA duplex is normally unattainable under isothermal conditions. As the progress of DNA-based nanoscience, methodology to control the DNA hybridization process has become increasingly important. Besides many reports using the chemically modified DNA for the regulation of hybridization, we focused our attention on the use of a small ligand as the molecular glue for the DNA. In 2001, we reported the first designed molecule that strongly and specifically bound to the mismatched base pairs in double stranded DNA. Further studies on the mismatch binding molecules provided us a key discovery of a novel mode of the binding of a mismatch binding ligand that induced the base flipping. With these findings we proposed the concept of molecular glue for DNA for the unidirectional control of DNA hybridization and, eventually photoswitchable molecular glue for DNA, which enabled the bidirectional control of hybridization under photoirradiation. In this tutorial review, we describe in detail how we integrated the mismatch binding ligand into photoswitchable molecular glue for DNA, and the application and perspective in DNA-based nanoscience.

Spacecraft Hybrid (Mixed-Actuator) Attitude Control Experiences on NASA Science Missions

Science.gov (United States)

Dennehy, Cornelius J.

2014-01-01

There is a heightened interest within NASA for the design, development, and flight implementation of mixed-actuator hybrid attitude control systems for science spacecraft that have less than three functional reaction wheel actuators. This interest is driven by a number of recent reaction wheel failures on aging, but what could be still scientifically productive, NASA spacecraft if a successful hybrid attitude control mode can be implemented. Over the years, hybrid (mixed-actuator) control has been employed for contingency attitude control purposes on several NASA science mission spacecraft. This paper provides a historical perspective of NASA's previous engineering work on spacecraft mixed-actuator hybrid control approaches. An update of the current situation will also be provided emphasizing why NASA is now so interested in hybrid control. The results of the NASA Spacecraft Hybrid Attitude Control Workshop, held in April of 2013, will be highlighted. In particular, the lessons learned captured from that workshop will be shared in this paper. An update on the most recent experiences with hybrid control on the Kepler spacecraft will also be provided. This paper will close with some future considerations for hybrid spacecraft control.

Optimization of Antennas using a Hybrid Genetic-Algorithm Space-Mapping Algorithm

DEFF Research Database (Denmark)

Pantoja, M.F.; Bretones, A.R.; Meincke, Peter

2006-01-01

A hybrid global-local optimization technique for the design of antennas is presented. It consists of the subsequent application of a Genetic Algorithm (GA) that employs coarse models in the simulations and a space mapping (SM) that refines the solution found in the previous stage. The technique...

Simplified ejector model for control and optimization

International Nuclear Information System (INIS)

Zhu Yinhai; Cai Wenjian; Wen Changyun; Li Yanzhong

2008-01-01

In this paper, a simple yet effective ejector model for a real time control and optimization of an ejector system is proposed. Firstly, a fundamental model for calculation of ejector entrainment ratio at critical working conditions is derived by one-dimensional analysis and the shock circle model. Then, based on thermodynamic principles and the lumped parameter method, the fundamental ejector model is simplified to result in a hybrid ejector model. The model is very simple, which only requires two or three parameters and measurement of two variables to determine the ejector performance. Furthermore, the procedures for on line identification of the model parameters using linear and non-linear least squares methods are also presented. Compared with existing ejector models, the solution of the proposed model is much easier without coupled equations and iterative computations. Finally, the effectiveness of the proposed model is validated by published experimental data. Results show that the model is accurate and robust and gives a better match to the real performances of ejectors over the entire operating range than the existing models. This model is expected to have wide applications in real time control and optimization of ejector systems

Presentation of electric motor and motor control technology for electric vehicles and hybrid vehicles; Denki jidosha hybrid sha yo motor oyobi motor seigyo gijutsu no shokai

Energy Technology Data Exchange (ETDEWEB)

Matsudaira, N.; Masakik, R.; Tajima, F. [Hitachi, Ltd., Tokyo (Japan)

1999-02-01

The authors have developed a motor drive system for electric vehicles and hybrid vehicles. This system consists of a permanent magnet type synchronous motor, an inverter using insulated gate bipolar transistors (IGBTs) and a controller based on a single-chip microcomputer. To achieve a compact and light weight synchronous motor, an internal permanent magnet type rotor structure was designed. This paper presents motor control technology for electric vehicles, such as an optimization method of field weakening control and a new current control method. (author)

Valence electronic structure of cobalt phthalocyanine from an optimally tuned range-separated hybrid functional.

Science.gov (United States)

Brumboiu, Iulia Emilia; Prokopiou, Georgia; Kronik, Leeor; Brena, Barbara

2017-07-28

We analyse the valence electronic structure of cobalt phthalocyanine (CoPc) by means of optimally tuning a range-separated hybrid functional. The tuning is performed by modifying both the amount of short-range exact exchange (α) included in the hybrid functional and the range-separation parameter (γ), with two strategies employed for finding the optimal γ for each α. The influence of these two parameters on the structural, electronic, and magnetic properties of CoPc is thoroughly investigated. The electronic structure is found to be very sensitive to the amount and range in which the exact exchange is included. The electronic structure obtained using the optimal parameters is compared to gas-phase photo-electron data and GW calculations, with the unoccupied states additionally compared with inverse photo-electron spectroscopy measurements. The calculated spectrum with tuned γ, determined for the optimal value of α = 0.1, yields a very good agreement with both experimental results and with GW calculations that well-reproduce the experimental data.

A Hybrid Genetic Algorithm Approach for Optimal Power Flow

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Sydulu Maheswarapu

2011-08-01

Full Text Available This paper puts forward a reformed hybrid genetic algorithm (GA based approach to the optimal power flow. In the approach followed here, continuous variables are designed using real-coded GA and discrete variables are processed as binary strings. The outcomes are compared with many other methods like simple genetic algorithm (GA, adaptive genetic algorithm (AGA, differential evolution (DE, particle swarm optimization (PSO and music based harmony search (MBHS on a IEEE30 bus test bed, with a total load of 283.4 MW. Its found that the proposed algorithm is found to offer lowest fuel cost. The proposed method is found to be computationally faster, robust, superior and promising form its convergence characteristics.

Full Gradient Solution to Adaptive Hybrid Control

Science.gov (United States)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2017-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Dual mode linguistic hedge fuzzy logic controller for an isolated wind-diesel hybrid power system with superconducting magnetic energy storage unit

International Nuclear Information System (INIS)

Thameem Ansari, M.Md.; Velusami, S.

2010-01-01

A design of dual mode linguistic hedge fuzzy logic controller for an isolated wind-diesel hybrid power system with superconducting magnetic energy storage unit is proposed in this paper. The design methodology of dual mode linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of linguistic hedges and hybrid genetic algorithm-simulated annealing algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically and can speed up the control result to fit the system demand. The hybrid genetic algorithm-simulated annealing algorithm is adopted to search the optimal linguistic hedge combination in the linguistic hedge module. Dual mode concept is also incorporated in the proposed controller because it can improve the system performance. The system with the proposed controller was simulated and the frequency deviation resulting from a step load disturbance is presented. The comparison of the proportional plus integral controller, fuzzy logic controller and the proposed dual mode linguistic hedge fuzzy logic controller shows that, with the application of the proposed controller, the system performance is improved significantly. The proposed controller is also found to be less sensitive to the changes in the parameters of the system and also robust under different operating modes of the hybrid power system.

A reconfigurable hybrid supervisory system for process control

International Nuclear Information System (INIS)

Garcia, H.E.; Ray, A.; Edwards, R.M.

1994-01-01

This paper presents a reconfigurable approach to decision and control systems for complex dynamic processes. The proposed supervisory control system is a reconfigurable hybrid architecture structured into three functional levels of hierarchy, namely, execution, supervision, and coordination. While the bottom execution level is constituted by either reconfigurable continuously varying or discrete event systems, the top two levels are necessarily governed by reconfigurable sets of discrete event decision and control systems. Based on the process status, the set of active control and supervisory algorithm is chosen. The reconfigurable hybrid system is briefly described along with a discussion on its implementation at the Experimental Breeder Reactor II of Argonne National Laboratory. A process control application of this hybrid system is presented and evaluated in an in-plant experiment

A reconfigurable hybrid supervisory system for process control

International Nuclear Information System (INIS)

Garcia, H.E.; Ray, A.; Edwards, R.M.

1994-01-01

This paper presents a reconfigurable approach to decision and control systems for complex dynamic processes. The proposed supervisory control system is a reconfigurable hybrid architecture structured into three functional levels of hierarchy, namely, execution, supervision, and coordination. While, the bottom execution level is constituted by either reconfigurable continuously varying or discrete event systems, the top two levels are necessarily governed by reconfigurable sets of discrete event decision and control systems. Based on the process status, the set of active control and supervisory algorithm is chosen. The reconfigurable hybrid system is briefly described along with a discussion on its implementation at the Experimental Breeder Reactor 2 of Argonne National Laboratory. A process control application of this hybrid system is presented and evaluated in an in-plant experiment

Optimizing Armed Forces Capabilities for Hybrid Warfare – New Challenge for Slovak Armed Forces

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Peter PINDJÁK

2015-09-01

Full Text Available The paper deals with the optimization of military capabilities of the Slovak Armed Forces for conducting operations in a hybrid conflict, which represents one of the possible scenarios of irregular warfare. Whereas in the regular warfare adversaries intend to eliminate the centers of gravity of each other, most often command and control structures, in irregular conflicts, the center of gravity shifts towards the will and cognitive perception of the target population. Hybrid warfare comprises a thoroughly planned combination of conventional military approaches and kinetic operations with subversive, irregular activities, including information and cyber operations. These efforts are often accompanied by intensified activities of intelligence services, special operation forces, and even mercenary and other paramilitary groups. The development of irregular warfare capabilities within the Slovak Armed Forces will require a progressive transformation process that may turn the armed forces into a modern and adaptable element of power, capable of deployment in national and international crisis management operations.

Development of an Integrated Cooling System Controller for Hybrid Electric Vehicles

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Chong Wang

2017-01-01

Full Text Available A hybrid electrical bus employs both a turbo diesel engine and an electric motor to drive the vehicle in different speed-torque scenarios. The cooling system for such a vehicle is particularly power costing because it needs to dissipate heat from not only the engine, but also the intercooler and the motor. An electronic control unit (ECU has been designed with a single chip computer, temperature sensors, DC motor drive circuit, and optimized control algorithm to manage the speeds of several fans for efficient cooling using a nonlinear fan speed adjustment strategy. Experiments suggested that the continuous operating performance of the ECU is robust and capable of saving 15% of the total electricity comparing with ordinary fan speed control method.

Oil Reservoir Production Optimization using Optimal Control

DEFF Research Database (Denmark)

Völcker, Carsten; Jørgensen, John Bagterp; Stenby, Erling Halfdan

2011-01-01

Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using the adjo...... reservoir using water ooding and smart well technology. Compared to the uncontrolled case, the optimal operation increases the Net Present Value of the oil field by 10%.......Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using...

Sizing and Optimization for Hybrid Central in South Algeria Based on Three Different Generators

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Chouaib Ammari

2017-11-01

Full Text Available In this paper, we will size an optimum hybrid central content three different generators, two on renewable energy (solar photovoltaic and wind power and two nonrenewable (diesel generator and storage system because the new central generator has started to consider the green power technology in order for best future to the world, this central will use all the green power resource available and distributes energy to a small isolated village in southwest of Algeria named “Timiaouine”. The consumption of this village estimated with detailed in two season; season low consumption (winter and high consumption (summer, the hybrid central will be optimized by program Hybrid Optimization Model for Electric Renewable (HOMER PRO, this program will simulate in two configuration, the first with storage system, the second without storage system and in the end the program HOMER PRO will choose the best configuration which is the mixture of both economic and ecologic configurations, this central warrants the energetic continuity of village. Article History: Received May 18th 2017; Received in revised form July 17th 2017; Accepted Sept 3rd 2017; Available online How to Cite This Article: Ammari, C., Hamouda,M., and Makhloufi,S. (2017 Sizing and Optimization for Hybrid Central in South Algeria Based on Three Different Generators. International Journal of Renewable Energy Development, 6(3, 263-272. http://doi.org/10.14710/ijred.6.3.263-272

Automated Model Generation for Hybrid Vehicles Optimization and Control Création automatique de modèles de composants pour l’optimisation et le contrôle de véhicules hybrides

Directory of Open Access Journals (Sweden)

Verdonck N.

2010-01-01

Full Text Available Systematic optimization of modern powertrains, and hybrids in particular, requires the representation of the system by means of Backward Quasistatic Models (BQM. In contrast, the models used in realistic powertrain simulators are often of the Forward Dynamic Model (FDM type. The paper presents a methodology to derive BQM’s of modern powertrain components, as parametric, steady-state limits of their FDM counterparts. The parametric nature of this procedure implies that changing the system modeled does not imply relaunching a simulation campaign, but only adjusting the corresponding parameters in the BQM. The approach is illustrated with examples concerning turbocharged engines, electric motors, and electrochemical batteries, and the influence of a change in parameters on the supervisory control of an hybrid vehicle is then studied offline, in co-simulation and on an HiL test bench adapted to hybrid vehicles (HyHiL. L’optimisation de l’utilisation des groupes moto-propulseurs (GMP modernes nécessite de modéliser le système de manière quasi-statique avec une logique inverse (“Backward Quasistatic Model” – BQM, en particulier dans le cas des GMP hybrides. Cependant, les modèles utilisés pour la simulation réaliste de ces GMP sont souvent dynamiques à logique directe (“Forward Dynamic Model” – FDM. Cet article présente une méthodologie pour obtenir les BQM des composants de GMP actuels directement issus de la limite quasi-statique des FDM correspondants de manière analytique. Grâce à l’aspect paramétrique de cette procédure, il n’est pas nécessaire de relancer une campagne de simulations après chaque changement du système modélisé: il suffit de modifier les paramètres correspondants dans le BQM. Cette approche est illustrée par trois cas d’étude (moteur turbo, moteur électrique et batterie, et l’effet d’un changement de paramètre sur le contrôle de supervision d’un véhicule hybride est �

On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles

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Imran Rahman

2016-03-01

Full Text Available Transportation electrification has undergone major changes since the last decade. Success of smart grid with renewable energy integration solely depends upon the large-scale penetration of plug-in hybrid electric vehicles (PHEVs for a sustainable and carbon-free transportation sector. One of the key performance indicators in hybrid electric vehicle is the State-of-Charge (SoC which needs to be optimized for the betterment of charging infrastructure using stochastic computational methods. In this paper, a newly emerged Accelerated particle swarm optimization (APSO technique was applied and compared with standard particle swarm optimization (PSO considering charging time and battery capacity. Simulation results obtained for maximizing the highly nonlinear objective function indicate that APSO achieves some improvements in terms of best fitness and computation time.

Hybrid Feedforward-Feedback Noise Control Using Virtual Sensors

Science.gov (United States)

Bean, Jacob; Fuller, Chris; Schiller, Noah

2016-01-01

Several approaches to active noise control using virtual sensors are evaluated for eventual use in an active headrest. Specifically, adaptive feedforward, feedback, and hybrid control structures are compared. Each controller incorporates the traditional filtered-x least mean squares algorithm. The feedback controller is arranged in an internal model configuration to draw comparisons with standard feedforward control theory results. Simulation and experimental results are presented that illustrate each controllers ability to minimize the pressure at both physical and virtual microphone locations. The remote microphone technique is used to obtain pressure estimates at the virtual locations. It is shown that a hybrid controller offers performance benefits over the traditional feedforward and feedback controllers. Stability issues associated with feedback and hybrid controllers are also addressed. Experimental results show that 15-20 dB reduction in broadband disturbances can be achieved by minimizing the measured pressure, whereas 10-15 dB reduction is obtained when minimizing the estimated pressure at a virtual location.

Optimal sizing of a hybrid grid-connected photovoltaic and wind power system

International Nuclear Information System (INIS)

González, Arnau; Riba, Jordi-Roger; Rius, Antoni; Puig, Rita

2015-01-01

Highlights: • Hybrid renewable energy systems are efficient mechanisms to generate electrical power. • This work optimally sizes hybrid grid-connected photovoltaic–wind power systems. • It deals with hourly wind, solar irradiation and electricity demand data. • The system cost is minimized while matching the electricity supply with the demand. • A sensitivity analysis to detect the most critical design variables has been done. - Abstract: Hybrid renewable energy systems (HRES) have been widely identified as an efficient mechanism to generate electrical power based on renewable energy sources (RES). This kind of energy generation systems are based on the combination of one or more RES allowing to complement the weaknesses of one with strengths of another and, therefore, reducing installation costs with an optimized installation. To do so, optimization methodologies are a trendy mechanism because they allow attaining optimal solutions given a certain set of input parameters and variables. This work is focused on the optimal sizing of hybrid grid-connected photovoltaic–wind power systems from real hourly wind and solar irradiation data and electricity demand from a certain location. The proposed methodology is capable of finding the sizing that leads to a minimum life cycle cost of the system while matching the electricity supply with the local demand. In the present article, the methodology is tested by means of a case study in which the actual hourly electricity retail and market prices have been implemented to obtain realistic estimations of life cycle costs and benefits. A sensitivity analysis that allows detecting to which variables the system is more sensitive has also been performed. Results presented show that the model responds well to changes in the input parameters and variables while providing trustworthy sizing solutions. According to these results, a grid-connected HRES consisting of photovoltaic (PV) and wind power technologies would be

Multi-objective energy management optimization and parameter sizing for proton exchange membrane hybrid fuel cell vehicles

International Nuclear Information System (INIS)

Hu, Zunyan; Li, Jianqiu; Xu, Liangfei; Song, Ziyou; Fang, Chuan; Ouyang, Minggao; Dou, Guowei; Kou, Gaihong

2016-01-01

Highlights: • Fuel economy, lithium battery size and powertrain system durability are incorporated in optimization. • A multi-objective power allocation strategy by taking battery size into consideration is proposed. • Influences of battery capacity and auxiliary power on strategy design are explored. • Battery capacity and fuel cell service life for the system life cycle cost are optimized. - Abstract: The powertrain system of a typical proton electrolyte membrane hybrid fuel cell vehicle contains a lithium battery package and a fuel cell stack. A multi-objective optimization for this powertrain system of a passenger car, taking account of fuel economy and system durability, is discussed in this paper. Based on an analysis of the optimum results obtained by dynamic programming, a soft-run strategy was proposed for real-time and multi-objective control algorithm design. The soft-run strategy was optimized by taking lithium battery size into consideration, and implemented using two real-time algorithms. When compared with the optimized dynamic programming results, the power demand-based control method proved more suitable for powertrain systems equipped with larger capacity batteries, while the state of charge based control method proved superior in other cases. On this basis, the life cycle cost was optimized by considering both lithium battery size and equivalent hydrogen consumption. The battery capacity selection proved more flexible, when powertrain systems are equipped with larger capacity batteries. Finally, the algorithm has been validated in a fuel cell city bus. It gets a good balance of fuel economy and system durability in a three months demonstration operation.

Finite-Control-Set Model Predictive Control (FCS-MPC) for Islanded Hybrid Microgrids

OpenAIRE

Yi, Zhehan; Babqi, Abdulrahman J.; Wang, Yishen; Shi, Di; Etemadi, Amir H.; Wang, Zhiwei; Huang, Bibin

2018-01-01

Microgrids consisting of multiple distributed energy resources (DERs) provide a promising solution to integrate renewable energies, e.g., solar photovoltaic (PV) systems. Hybrid AC/DC microgrids leverage the merits of both AC and DC power systems. In this paper, a control strategy for islanded multi-bus hybrid microgrids is proposed based on the Finite-Control-Set Model Predictive Control (FCS-MPC) technologies. The control loops are expedited by predicting the future states and determining t...

Strategy and gaps for modeling, simulation, and control of hybrid systems

Energy Technology Data Exchange (ETDEWEB)

Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Garcia, Humberto E. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Hovsapian, Rob [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kinoshita, Robert [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mesina, George L. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Bragg-Sitton, Shannon M. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Boardman, Richard D. [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-04-01

The purpose of this report is to establish a strategy for modeling and simulation of candidate hybrid energy systems. Modeling and simulation is necessary to design, evaluate, and optimize the system technical and economic performance. Accordingly, this report first establishes the simulation requirements to analysis candidate hybrid systems. Simulation fidelity levels are established based on the temporal scale, real and synthetic data availability or needs, solution accuracy, and output parameters needed to evaluate case-specific figures of merit. Accordingly, the associated computational and co-simulation resources needed are established; including physical models when needed, code assembly and integrated solutions platforms, mathematical solvers, and data processing. This report first attempts to describe the figures of merit, systems requirements, and constraints that are necessary and sufficient to characterize the grid and hybrid systems behavior and market interactions. Loss of Load Probability (LOLP) and effective cost of Effective Cost of Energy (ECE), as opposed to the standard Levelized Cost of Electricty (LCOE), are introduced as technical and economical indices for integrated energy system evaluations. Financial assessment methods are subsequently introduced for evaluation of non-traditional, hybrid energy systems. Algorithms for coupled and iterative evaluation of the technical and economic performance are subsequently discussed. This report further defines modeling objectives, computational tools, solution approaches, and real-time data collection and processing (in some cases using real test units) that will be required to model, co-simulate, and optimize; (a) an energy system components (e.g., power generation unit, chemical process, electricity management unit), (b) system domains (e.g., thermal, electrical or chemical energy generation, conversion, and transport), and (c) systems control modules. Co-simulation of complex, tightly coupled

A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays

Directory of Open Access Journals (Sweden)

An Liu

2012-01-01

Full Text Available Coordination optimization of directional overcurrent relays (DOCRs is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS and pickup current (Ip values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO considers the optimal solution as the final solution, and an early convergence of PSO results in decreased overall performance and an increase in the risk of mistaking local optima for global optima. Therefore, this study proposes a new hybrid Nelder-Mead simplex search method and particle swarm optimization (proposed NM-PSO algorithm to solve the DOCR coordination optimization problem. PSO is the main optimizer, and the Nelder-Mead simplex search method is used to improve the efficiency of PSO due to its potential for rapid convergence. To validate the proposal, this study compared the performance of the proposed algorithm with that of PSO and original NM-PSO. The findings demonstrate the outstanding performance of the proposed NM-PSO in terms of computation speed, rate of convergence, and feasibility.

Modeling and control of a hybrid-electric vehicle for drivability and fuel economy improvements

Science.gov (United States)

Koprubasi, Kerem

The gradual decline of oil reserves and the increasing demand for energy over the past decades has resulted in automotive manufacturers seeking alternative solutions to reduce the dependency on fossil-based fuels for transportation. A viable technology that enables significant improvements in the overall tank-to-wheel vehicle energy conversion efficiencies is the hybridization of electrical and conventional drive systems. Sophisticated hybrid powertrain configurations require careful coordination of the actuators and the onboard energy sources for optimum use of the energy saving benefits. The term optimality is often associated with fuel economy, although other measures such as drivability and exhaust emissions are also equally important. This dissertation focuses on the design of hybrid-electric vehicle (HEV) control strategies that aim to minimize fuel consumption while maintaining good vehicle drivability. In order to facilitate the design of controllers based on mathematical models of the HEV system, a dynamic model that is capable of predicting longitudinal vehicle responses in the low-to-mid frequency region (up to 10 Hz) is developed for a parallel HEV configuration. The model is validated using experimental data from various driving modes including electric only, engine only and hybrid. The high fidelity of the model makes it possible to accurately identify critical drivability issues such as time lags, shunt, shuffle, torque holes and hesitation. Using the information derived from the vehicle model, an energy management strategy is developed and implemented on a test vehicle. The resulting control strategy has a hybrid structure in the sense that the main mode of operation (the hybrid mode) is occasionally interrupted by event-based rules to enable the use of the engine start-stop function. The changes in the driveline dynamics during this transition further contribute to the hybrid nature of the system. To address the unique characteristics of the HEV

A Hybrid Optimization Method for Solving Bayesian Inverse Problems under Uncertainty.

Directory of Open Access Journals (Sweden)

Kai Zhang

Full Text Available In this paper, we investigate the application of a new method, the Finite Difference and Stochastic Gradient (Hybrid method, for history matching in reservoir models. History matching is one of the processes of solving an inverse problem by calibrating reservoir models to dynamic behaviour of the reservoir in which an objective function is formulated based on a Bayesian approach for optimization. The goal of history matching is to identify the minimum value of an objective function that expresses the misfit between the predicted and measured data of a reservoir. To address the optimization problem, we present a novel application using a combination of the stochastic gradient and finite difference methods for solving inverse problems. The optimization is constrained by a linear equation that contains the reservoir parameters. We reformulate the reservoir model's parameters and dynamic data by operating the objective function, the approximate gradient of which can guarantee convergence. At each iteration step, we obtain the relatively 'important' elements of the gradient, which are subsequently substituted by the values from the Finite Difference method through comparing the magnitude of the components of the stochastic gradient, which forms a new gradient, and we subsequently iterate with the new gradient. Through the application of the Hybrid method, we efficiently and accurately optimize the objective function. We present a number numerical simulations in this paper that show that the method is accurate and computationally efficient.

A hybrid flight control for a simulated raptor-30 v2 helicopter

International Nuclear Information System (INIS)

Khizer, A.N.

2015-01-01

This paper presents a hybrid flight control system for a single rotor simulated Raptor-30 V2 helicopter. Hybrid intelligent control system, combination of the conventional and intelligent control methodologies, is applied to small model helicopter. The proposed hybrid control used PID as a traditional control and fuzzy as an intelligent control so as to take the maximum advantage of advanced control theory. The helicopter model used; comes from X-Plane flight simulator and their hybrid flight control system was simulated using MATLAB/SIMULINK in a simulation platform. X-Plane is also used to visualize the performance of this proposed autopilot design. Through a series of numerous experiments, the operation of hybrid control system was investigated. Results verified that the proposed hybrid control has an excellent performance at hovering flight mode. (author)

Honey/PVA hybrid wound dressings with controlled release of antibiotics: Structural, physico-mechanical and in-vitro biomedical studies.

Science.gov (United States)

Tavakoli, Javad; Tang, Youhong

2017-08-01

Hydrogel/honey hybrids manifest an attractive design with an exclusive therapeutic property that promotes wound healing process. The greater the concentration of honey within the formulation, the better the biomedical properties that will be achieved. However, an increase in the percentage of honey can negatively affect the physico-chemical and mechanical properties of hybrid hydrogels. The need exists, therefore, to prepare wound dressings that contain high honey density with optimal biomedical, mechanical and physicochemical properties. In this study, a simple method for the preparation of a highly concentrated honey/PVA hybrid hydrogel with borax as the crosslinking agent is reported. Comprehensive evaluations of the morphology, swelling kinetics, permeability, bio-adhesion, mechanical characteristics, cytotoxicity, antibacterial property, cell proliferation ability and their controlling release properties were conducted as a function of crosslinking density. All the borax-induced hydrogels showed acceptable biocompatibility, and the incorporation of 1% borax in the hydrogel formulation produced optimal behaviours for wound addressing applications. Copyright © 2017 Elsevier B.V. All rights reserved.

[Optimization and assessment of a reverse hybridization system for the detection of HBV drug-resistant mutations].

Science.gov (United States)

Liu, Yan-chen; Huang, Ai-long; Hu, Yuan; Hu, Jie-li; Lai, Guo-qi; Zhang, Wen-lu

2011-12-01

To establish a detection method for HBV drug-resistant mutations related to lamivudine, adefovir and entecavir by optimization and assessment of reverse hybridization system. 26 degenerated probes covering 10 drug-resistant hotspots of 3 drugs were synthesized and immobilized on the same positively charged nylon membrane. PCR products labeled with digoxigenin were hybridized with corresponding probes. To improve the sensitivity and specificity, 4 reaction steps of reverse hybridization were optimized including the number of labeled digoxigenin, the energy intensity of UV cross-linking, hybridization and stringency wash conditions. To prove the feasibility, the specificity, sensitivity and accuracy of this system were assessed respectively. Sensitive and specific results are obtained by the optimization of the following 4 reaction steps: the primers labeled with 3 digoxigenin, energy intensity of UV cross-linking for 1500 x 0.1 mJ/cm², hybridization at 42 degrees C and stringency wash with 0.5 x SSC and 0.1% SDS solution at 44 degrees C for 30 min. In the assessment of system, the majority of probes have high specificity. The quantity of PCR product with a concentration of 10 ng/μl or above can be detected by this method. The concordant rate between reverse hybridization and direct sequencing is 93.9% in the clinical sample test. Though the specificity of several probes needs to be improved further, it is a simple, rapid and sensitive method which can detect HBV resistant mutations related to lamivudine, adefovir and entecavir simultaneously. Due to the short distance between 180 and 181, likewise 202 and 204, the sequence of the same probe covers two codon positions, and hybridization will be interfered by each other. To avoid such interference, the possible solution is that probes are designed by arranging and combining various forms of two near codons.

A novel probe density controllable electrochemiluminescence biosensor for ultra-sensitive detection of Hg2+ based on DNA hybridization optimization with gold nanoparticles array patterned self-assembly platform.

Science.gov (United States)

Gao, Wenhua; Zhang, An; Chen, Yunsheng; Chen, Zixuan; Chen, Yaowen; Lu, Fushen; Chen, Zhanguang

2013-11-15

Biosensor based on DNA hybridization holds great potential to get higher sensitivity as the optimal DNA hybridization efficiency can be achieved by controlling the distribution and orientation of probe strands on the transducer surface. In this work, an innovative strategy is reported to tap the sensitivity potential of current electrochemiluminescence (ECL) biosensing system by dispersedly anchoring the DNA beacons on the gold nanoparticles (GNPs) array which was electrodeposited on the glassy carbon electrode surface, rather than simply sprawling the coil-like strands onto planar gold surface. The strategy was developed by designing a "signal-on" ECL biosensing switch fabricated on the GNPs nanopatterned electrode surface for enhanced ultra-sensitivity detection of Hg(2+). A 57-mer hairpin-DNA labeled with ferrocene as ECL quencher and a 13-mer DNA labeled with Ru(bpy)3(2+) as reporter were hybridized to construct the signal generator in off-state. A 31-mer thymine (T)-rich capture-DNA was introduced to form T-T mismatches with the loop sequence of the hairpin-DNA in the presence of Hg(2+) and induce the stem-loop open, meanwhile the ECL "signal-on" was triggered. The peak sensitivity with the lowest detection limit of 0.1 nM was achieved with the optimal GNPs number density while exorbitant GNPs deposition resulted in sensitivity deterioration for the biosensor. We expect the present strategy could lead the renovation of the existing probe-immobilized ECL genosensor design to get an even higher sensitivity in ultralow level of target detection such as the identification of genetic diseases and disorders in basic research and clinical application. Copyright © 2013 Elsevier B.V. All rights reserved.

Hybrid glowworm swarm optimization for task scheduling in the cloud environment

Science.gov (United States)

Zhou, Jing; Dong, Shoubin

2018-06-01

In recent years many heuristic algorithms have been proposed to solve task scheduling problems in the cloud environment owing to their optimization capability. This article proposes a hybrid glowworm swarm optimization (HGSO) based on glowworm swarm optimization (GSO), which uses a technique of evolutionary computation, a strategy of quantum behaviour based on the principle of neighbourhood, offspring production and random walk, to achieve more efficient scheduling with reasonable scheduling costs. The proposed HGSO reduces the redundant computation and the dependence on the initialization of GSO, accelerates the convergence and more easily escapes from local optima. The conducted experiments and statistical analysis showed that in most cases the proposed HGSO algorithm outperformed previous heuristic algorithms to deal with independent tasks.

Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

Science.gov (United States)

Li, Ze

2017-09-01

In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.

Optimal energy management for a mechanical-hybrid vehicle with cold start conditions

NARCIS (Netherlands)

Berkel, van K.; Klemm, W.P.A.; Hofman, T.; Vroemen, B.G.; Steinbuch, M.

2013-01-01

This paper presents the design of an optimal Energy Management Strategy (EMS) for a hybrid vehicle that starts with a cold powertrain. The cold start negatively affects the combustion and transmission efficiency of the powertrain, caused by the higher frictional losses due to increased hydrodynamic

Artificial root foraging optimizer algorithm with hybrid strategies

Directory of Open Access Journals (Sweden)

Yang Liu

2017-02-01

Full Text Available In this work, a new plant-inspired optimization algorithm namely the hybrid artificial root foraging optimizion (HARFO is proposed, which mimics the iterative root foraging behaviors for complex optimization. In HARFO model, two innovative strategies were developed: one is the root-to-root communication strategy, which enables the individual exchange information with each other in different efficient topologies that can essentially improve the exploration ability; the other is co-evolution strategy, which can structure the hierarchical spatial population driven by evolutionary pressure of multiple sub-populations that ensure the diversity of root population to be well maintained. The proposed algorithm is benchmarked against four classical evolutionary algorithms on well-designed test function suites including both classical and composition test functions. Through the rigorous performance analysis that of all these tests highlight the significant performance improvement, and the comparative results show the superiority of the proposed algorithm.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

Science.gov (United States)

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

Targeted 2D/3D registration using ray normalization and a hybrid optimizer

International Nuclear Information System (INIS)

Dey, Joyoni; Napel, Sandy

2006-01-01

X-ray images are often used to guide minimally invasive procedures in interventional radiology. The use of a preoperatively obtained 3D volume can enhance the visualization needed for guiding catheters and other surgical devices. However, for intraoperative usefulness, the 3D dataset needs to be registered to the 2D x-ray images of the patient. We investigated the effect of targeting subvolumes of interest in the 3D datasets and registering the projections with C-arm x-ray images. We developed an intensity-based 2D/3D rigid-body registration using a Monte Carlo-based hybrid algorithm as the optimizer, using a single view for registration. Pattern intensity (PI) and mutual information (MI) were two metrics tested. We used normalization of the rays to address the problems due to truncation in 3D necessary for targeting. We tested the algorithm on a C-arm x-ray image of a pig's head and a 3D dataset reconstructed from multiple views of the C-arm. PI and MI were comparable in performance. For two subvolumes starting with a set of initial poses from +/-15 mm in x, from +/-3 mm (random), in y and z and +/-4 deg in the three angles, the robustness was 94% for PI and 91% for MI, with accuracy of 2.4 mm (PI) and 2.6 mm (MI), using the hybrid algorithm. The hybrid optimizer, when compared with a standard Powell's direction set method, increased the robustness from 59% (Powell) to 94% (hybrid). Another set of 50 random initial conditions from [+/-20] mm in x,y,z and [+/-10] deg in the three angles, yielded robustness of 84% (hybrid) versus 38% (Powell) using PI as metric, with accuracies 2.1 mm (hybrid) versus 2.0 mm (Powell)

Neural-network hybrid control for antilock braking systems.

Science.gov (United States)

Lin, Chih-Min; Hsu, C F

2003-01-01

The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

Thermo-economic optimization of a hybrid solar district heating plant with flat plate collectors and parabolic trough collectors in series

DEFF Research Database (Denmark)

Tian, Zhiyong; Perers, Bengt; Furbo, Simon

2018-01-01

heating network in this study. The results also show that parabolic trough collectors are economically feasible for district heating networks in Denmark. The generic and multivariable levelized cost of heat method can guide engineers and designers on the design, construction and control of large...... to optimize the hybrid solar district heating systems based on levelized cost of heat. It is found that the lowest net levelized cost of heat of hybrid solar heating plants could reach about 0.36 DKK/kWh. The system levelized cost of heat can be reduced by 5–9% by use of solar collectors in the district...

Cooperative Control of Regenerative Braking and Antilock Braking for a Hybrid Electric Vehicle

Directory of Open Access Journals (Sweden)

Guodong Yin

2013-01-01

Full Text Available A new cooperative braking control strategy (CBCS is proposed for a parallel hybrid electric vehicle (HEV with both a regenerative braking system and an antilock braking system (ABS to achieve improved braking performance and energy regeneration. The braking system of the vehicle is based on a new method of HEV braking torque distribution that makes the antilock braking system work together with the regenerative braking system harmoniously. In the cooperative braking control strategy, a sliding mode controller (SMC for ABS is designed to maintain the wheel slip within an optimal range by adjusting the hydraulic braking torque continuously; to reduce the chattering in SMC, a boundary-layer method with moderate tuning of a saturation function is also investigated; based on the wheel slip ratio, battery state of charge (SOC, and the motor speed, a fuzzy logic control strategy (FLC is applied to adjust the regenerative braking torque dynamically. In order to evaluate the performance of the cooperative braking control strategy, the braking system model of a hybrid electric vehicle is built in MATLAB/SIMULINK. It is found from the simulation that the cooperative braking control strategy suggested in this paper provides satisfactory braking performance, passenger comfort, and high regenerative efficiency.