Advanced deep deterministic policy gradient based energy management strategy design for dual-motor four-wheel-drive electric vehicle

Abstract

Four-wheel-drive battery electric vehicles (BEV) driven by multiple motors on different axles are getting popular by offering outstanding dynamic and safety performance without sacrificing structure complexity. However, efficiently splitting the power flow between power sources is crucial and difficult. In this study, an intelligent energy management strategy (EMS) is proposed for a specific dual-motor four-wheel-drive (DM-4WD) BEV to reduce energy consumption in unknown traffic conditions. A novel reward factor involved deep deterministic policy gradient (DDPG) algorithm is proposed in EMS design, whose parameters matching are based on particle swarm optimization algorithm to provide a platform to investigate the maximum potential of energy performance improvement for the proposed EMS. The simulation results show that the proposed DDPG-EMS reaches 95.7%, 94.8%, and 95.5% of benchmark dynamic programming-EMS energy performance and outperforms the discontinued-action-based double deep Q-learning strategy in unknow driving cycles. Furthermore, the adaptability of DDPG-EMS is improved by introducing novel rewards setting, which is 3%, 3.8%, and 2.4% better than the traditional State-of-Charge (SOC)-based DDPG-EMS. The simulation results suggest the proposed strategy is efficient and instructive for multi-power BEV EMS design.