Driving-Style-Oriented Energy Management Strategy Based on Fuzzy Logic for Plug-in Hybrid Electric Buses

The driving style has a significant influence on vehicle energy management, especially in conditions where the driving cycle is indeterminate. To achieve the optimal fuel consumption in the face of different styles of drivers, this article proposed a new energy management strategy (EMS) considering both the driving style and equivalent factor (EF) bounds in equivalent consumption minimization strategy (ECMS). First, based on the influence of characteristic parameters on style determination, a trustworthy three-parameter driving style recognition system is constructed by combining K-means clustering and fuzzy logic. Then, a large number of driving cycles are simulated and counted, and the gray wolf optimization (GWO) algorithm is introduced to get the optimal EF value of each driving cycle. On that basis, an adaptive ECMS (A-ECMS) is designed. The A-ECMS method uses EF boundaries to limit fuel consumption for different driving rounds, with driving styles adapted to different driving cycles. Finally, online simulation proves the feasibility of the proposed EMS. Compared with another A-ECMS and the optimal EF obtained by the optimization algorithm, the EMS has a significant improvement in fuel economy. The comparison results of hardware-in-the-loop (HIL) testing also show that the fuel efficiency of the proposed strategy is consistent with the original design intent.