Title :
Parameter Optimization of Power Control Strategy for Series Hybrid Electric Vehicle
Author :
Huang, Bufu ; Shi, Xi ; Xu, Yangsheng
Author_Institution :
Chinese Univ. of Hong Kong, Shatin
Abstract :
Aimed at the more and more serious problems of energy and pollution, Hybrid Electric Vehicle (HEV) is one of the best practical applications for transportation with high fuel economy and low emission. Since the power control strategy has a critical effect on the performance of HEV, genetic algorithm is introduced to optimize the strategy parameters for fuel economy and emissions in this paper. Compared with two main strategies, Thermostatic and DIRECT, the computation procedures of genetic algorithm are discussed, and simulation study based on the model of series hybrid electric vehicle is given to illustrate the optimization validity of the genetic algorithm.
Keywords :
air pollution; genetic algorithms; hybrid electric vehicles; power control; fuel economy; genetic algorithm; parameter optimization; power control strategy; series hybrid electric vehicle; transportation; Automatic control; Batteries; Engines; Fuel economy; Genetic algorithms; Hybrid electric vehicles; Optimization methods; Pollution; Power control; Transportation;
Conference_Titel :
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9487-9
DOI :
10.1109/CEC.2006.1688551
