Fuzzy control strategy for hybrid electric vehicle based on neural network identi cation of driving conditions
神经网络工况识别的混合动力电动汽车模糊控制策略

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 identi cation 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 identi cation of driving conditions improves the fuel consumption and reduces the emission.