《【2016.05.19】【工况识别】神经网络工况识别的溷合动力电动汽车模煳控制策略》.pdf

28 3 Vol. 28 No. 3 2011 3 Control Theory Applications Mar. 2011 :2011 , , , (, 100044) : (HEV), . , . , , , , . , , , HEV . : ; ; ; ; : U469.72 : A Fuzzy control strategy for hybrid electric vehicle based on neural network identification of driving conditions TIAN Yi, ZHANG Xin, ZHANG Liang, ZHANG Xin (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China) Abstract: The fuzzy control strategy can improve the fuel consumption and reduce the emission of hybrid electric vehicle(HEV), but the parameters of control strategy are always optimized under a typical driving condition which is different from different cities. We study the fuzzy control strategy based on the urban driving conditions of Guangzhou and Shanghai. First, we propose a fuzzy control strategy and optimize the parameters of membership functions by applying the genetic algorithm to the urban driving conditions in Guangzhou and Shanghai. Second, we identify the urban driving conditions in these two cities based on the fuzzy neural network. The results of identification are applied to adjust the parameters of membership functions in the fuzzy control strategy for the HEV. The simulation results show that the HEV fuzzy control strategy based on the fuzzy neural network identification of driving conditions improves the fuel consumption and reduces the emission. Key words: hybrid electric vehicle; driving cycle; fuzzy control; neural network; genetic algorithm 1 (Introduction) M. DuobaHEV [2] (HEV)21 .