Health-awareness energy management strategy for battery electric vehicles based on self-attention deep reinforcement learning

Abstract

The economical and safe energy management strategy (EMS) is essential for a battery electric vehicle (BEV). With an improved deep deterministic policy gradient (DDPG)-based EMS proposed for a dual-motor BEV, energy consumption and electric component aging are minimized. Firstly, this study incorporates health awareness of electric components into EMS. Then, this study employs a gated recurrent unit (GRU) as well as a self-attention (SA) mechanism for improving the original DDPG's performance. The GRU enhances the performance of DDPG agents, post-convergence, by furnishing them with additional state information. The SA mechanism empowers agents to distinguish the importance of state information, thus improving its adaptability in new environments. The proposed EMS decreases battery energy consumption to 7.95 kW-hour and enhances battery state of health (SOH) to 96.83 % of the dynamic programming (DP)-based EMS. The two motors of the proposed EMS possess the highest end-value SOH, i.e., 99.99929 % and 99.99978 %, respectively. The proposed EMS demonstrates superior adaptability to other EMSs, regardless of the initial state of charge (SOC). It shows the best adaptability at an initial SOC of 0.6, and achieves 95.87 % and 94.67 % of the energy consumption of DP-based EMS in the mixed and actual test cycles, respectively.