scholarly journals Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 426 ◽  
Author(s):  
Yongliang Zheng ◽  
Feng He ◽  
Xinze Shen ◽  
Xuesheng Jiang
Keyword(s):  
Support Vector Machine ◽  
Fuel Cell ◽  
Fuzzy Control ◽  
Working Conditions ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Control Strategies ◽  
Least Square ◽  
Support Vector ◽  
Fuzzy Control Strategy

Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried out for typical working conditions, and working conditions were identified by the least square support vector machine (LSSVM) optimized by grid search and cross validation (CV). The corresponding fuzzy control strategies were formulated under different typical working conditions, in addition, the fuzzy control strategy was optimized with total equivalent energy consumption as the goal by particle swarm optimization (PSO). The adaptive switching of fuzzy control strategies under different working conditions were realized through the identification of driving conditions. Results showed that the fuzzy control strategy with the function of driving conditions identification had a more efficient power distribution and better economy.

scholarly journals Research on Genetic-fuzzy Control Strategy for Parallel Hybrid Electric Vehicle

2010 ◽  
Vol 4 (1) ◽  
pp. 224-231 ◽  
Author(s):  
Shichun Yang ◽  
Ming Li ◽  
Haoyu Weng ◽  
Bao Liu ◽  
Qiang Li ◽  
...  
Keyword(s):  
Fuzzy Control ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Fuzzy Control Strategy

A Research on the Fuzzy Control Strategy for Parallel Hybrid Electric Vehicle

2011 ◽  
Vol 121-126 ◽  
pp. 2522-2526
Author(s):  
Ling Cai ◽  
Liang Ge
Keyword(s):  
Fuzzy Control ◽  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Secondary Development ◽  
Simulation Results ◽  
Hybrid Electric ◽  
Fuzzy Control Strategy

Several kinds of methods of energy management of hybrid electric vehicle (HEV) are analyzed. Based on the design requirement of a certain type of parallel HEV, the fuzzy control strategy of energy management is proposed. ADVISOR2002 is chosen as the simulation platform for secondary development, and the simulation results of the fuzzy control strategy and electric assist control strategy are compared. The simulation results indicate that the adaptive fuzzy controller can obviously improve the performance of HEV fuel economy and emissions.

Study Performance of Pure Electric Buses Based on the Fuzzy Control Strategy

2011 ◽  
Vol 301-303 ◽  
pp. 1482-1488
Author(s):  
Feng Jun Zhou ◽  
Cheng Lin ◽  
Jin Rui Nan ◽  
Gang Wang ◽  
Wan Ke Cao
Keyword(s):  
Fuzzy Control ◽  
Control Strategy ◽  
Control Strategies ◽  
Dynamic Performance ◽  
Excellent Performance ◽  
Driving System ◽  
Electric Bus ◽  
Fuzzy Control Strategy ◽  
Study Performance

Pure electric bus control strategies and methods have an important effect on performance of the bus. The pure electric buses vehicle driving system was modeled and optimized by the fuzzy control strategy. Through simulation and commissioning tests, both the dynamic performance and economy of the pure electrical bus that using fuzzy control strategy have an excellent performance.

Real Time Control Strategy for Hybrid Electric Vehicles

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Fuzzy Control ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Complex Structure ◽  
Battery Charge ◽  
Hybrid Car ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Fuzzy Control Strategy

Based on the complex structure of electric hybrid car and uncertainty in driving force structure of electric hybrid car, different strategies have been presented for optimal management of energy based on smart methods. In this study by the decision making nature of fuzz logic, a movement map for Parallel Hybrid Electric Vehicle (PHEV) is made based on the required path. In a parallel hybrid car, recharging control of battery and auxiliary torque of electric engine are used as the key points of movement. Based on the disadvantages of pure electric car, to increase the life of battery and its easy use, we need a movement strategy balancing the battery charge for a movement path. If the battery is charged at no load by the combustion engine, NOx emission is increased and the battery charge is not good and adequate for HEV performance under no-load condition by the energy retrieval power and combustion engine. For a movement structure, it is hard to define the conversion point between the motor performance and generator performance exactly. By a drive strategy based on crisp methods, the battery charge is sensitive to the moving samples of driver, path condition and load conditions. Using fuzzy control strategy to control varied non-linear systems is very suitable and it is robust against the changes of components of sub-systems and inexact measurements. New York City Cycle (NYCC) is considered to perform simulation. As shown in paper, the fuzzy control strategy can keep the charge stage of batteries at good range.

scholarly journals Application of Support Vector Machine-Based Semiactive Control for Seismic Protection of Structures with Magnetorheological Dampers

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Chunxiang Li ◽  
Qing Liu ◽  
Shengning Lan
Keyword(s):  
Support Vector Machine ◽  
Control Strategy ◽  
Control Strategies ◽  
Seismic Protection ◽  
Support Vector ◽  
Semiactive Control ◽  
Mr Dampers ◽  
Acceleration Amplification ◽  
Switch Control ◽  
Lqr Controller

Based on recent research by Li and Liu in 2011, this paper proposes the application of support vector machine- (SVM-) based semiactive control methodology for seismic protection of structures with magnetorheological (MR) dampers. An important and challenging task of designing the MR dampers is to develop an effective semiactive control strategy that can fully exploit the capabilities of MR dampers. However, amplification of the local acceleration response of structures exists in the widely used semiactive control strategies, namely “Switch” control strategies. Then the SVM-based semiactive control strategy has been employed to design MR dampers. Firstly, the LQR controller for the numerical model of a multistory structure formulated using the dynamic dense method is constructed by using the classic LQR control theory. Secondly, an SVM model which comprises the observers and controllers in the control system is designed and trained to emulate the performance of the LQR controller. Finally, an online autofeedback semiactive control strategy is developed by resorting to SVM and then used for designing MR dampers. Simulation results show that the MR dampers utilizing the SVM-based semiactive control algorithm, which eliminates the local acceleration amplification phenomenon, can remarkably reduce the displacement, velocity, and acceleration responses of the structure.

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