Predictive and Coordinated Power Control Strategy for Series Hybrid Electric Vehicle with Fuzzy Adaptive Filter

The power control strategy (PCS) is a key technology of series hybrid electric vehicle (SHEV) owing to the ability to coordinate the power flow between multi-energy sources to reduce fuel consumption. When power allocation command of PCS fluctuates sharply, engine-generator set (EGS) might not output the power allocated by the PCS in time, due to the dynamic response lag of engine. In this case, it would cause the lack of vehicle driving power. Therefore, how to ensure smooth power output of EGS and obtain the optimal fuel economy is a challenge. To solve this problem, a predictive and coordinated power control strategy for SHEV is proposed in this study. A predictive adaptive equivalent consumption minimization strategy (PA-ECMS) is proposed to optimize power flow in real time, in which long short-term memory network is applied to predict future demand power to adjust the equivalent factor. In order to reduce the fluctuation of power allocation command, an adaptive fuzzy controller is designed to modify the working points of engine according to the optimizing results of PA-ECMS. The results show that the power command of PCS can be smoothed, and the fuel economy can be improved by 4.91% over conventional ECMS.