Research on Adaptive Energy Management Strategy Based on Road Segment Electricity Allocation for the PHEV

Fuel efficiency enhancement in plug-in hybrid electric vehicles (PHEVs) is intricately linked to battery usage efficiency and distinct driving conditions. Aiming at a multi-gear and multi-mode parallel PHEV, this study proposes an adaptive energy management strategy based on the battery electricity allocation and equivalent fuel consumption minimization strategy (ECMS) considering the characteristics of different road segments. First, different kinds of standard driving cycles are segmented and clustered into four types, on which basis a combination cycle of different types is constructed. Using dynamic programming, the effects of different average available battery state of charge (SOC) and different driving condition ratios on the optimal battery electricity allocation are studied. Second, a neural network is used to establish a prediction model for the road segment electricity allocation and the battery SOC is planned. Finally, the ECMS strategy is adopted for optimal torque allocation, whose equivalent factor is optimized by the genetic algorithm. Research results show that, when the initial SOC of the battery is 0.9 and 0.8, the proposed energy management strategy improves the vehicle fuel economy by 5.35% and 5.72%, respectively, compared with the ECMS strategy that plans the SOC trajectory based on the traveling distance.