Delayed Deep Deterministic Policy Gradient-Based Energy Management Strategy for Overall Energy Consumption Optimization of Dual Motor Electrified Powertrain


Jiageng Ruan, Changcheng Wu, Hanghang Cui, Weihan Li, Dirk Uwe Sauer, IEEE Transactions on Vehicular Technology, Vol. 72, 9 September 2023.



The anxiety-provoking driving range has always been an obstacle to the large-scale popularization of electric vehicles (EVs). To improve the driving range without affecting the driving performance, a Dual-Motor Two-Speed All-Wheel-Drive (DMTS-AWD) electrified powertrain is proposed in this work. The system adopts a motor on the front axle and rear axle, respectively, and the rear motor adopts a two-speed automatic mechanical transmission to improve energy efficiency and dynamic performance. An advanced Delayed Deep Deterministic Policy Gradient (TD3)-based Energy Management Strategy (EMS) is used to pursue better motor working efficiency with consideration of practicability. In addition, a direct control method without complicating the structure of the actor network is proposed to realize mode selection and torque distribution simultaneously, which is a discrete(mode)-continuous (motor torque) hybrid action space. The simulation results show that the energy consumption of DMTS-AWD with TD3-based EMS improves by 6.44% compared to the Single-Motor Single-Speed (SMSS) powertrain, which is comparable to the global optimization method. Moreover, the proposed EMS in this paper has a faster convergence speed and better adaptability compared with the classic Deep Deterministic Policy Gradient (DDPG).