Deep reinforcement learning based energy management strategy for range extend fuel cell hybrid electric vehicle. (1st February 2023) - Huang, Yin
Highlight: A new topology structure and operating pattern using fuel cell as range extenders is proposed. An energy management strategy framework based on deep reinforcement learning with dual DDPG switching is proposed. The previous action guidance mechanism can enhance the global optimal search and the stability of convergence. Validation results denote that fuel economy and durability of battery and fuel cell are improved in both two modes. Abstract: To meet the power and long-range driving requirements of the vehicle, this paper presents a dual mode operation scheme for a range extend fuel cell hybrid vehicle for the first time, with an in-depth study of the pure electric mode and the range extend mode. The deep deterministic policy gradient algorithm is a well-known deep reinforcement learning algorithm that can solve complex nonlinear problems. To achieve the optimal power distribution among energy sources in the two modes, a dual deep deterministic policy gradient algorithm framework is proposed for the first time in this paper. In addition, a pervious action guidance mechanism is proposed to enable networks to approximate the action value function more efficiently in training. The training results show that the adopted previous action guidance mechanism helps to improve the learning convergence and exploration ability. The validation results show that the proposed strategy improves the operating economy by about 30% compared to the rule-based strategy, reduces the average fuel cell output fluctuation to less than 100 W, and reduces the fuel cell lifetime loss greatly. It is hoped that the proposed new structure, patterns, and energy management strategy will provide more ideas for scholars in future research.