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
Abstract
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).