Deep reinforcement learning based energy management strategy for range extend fuel cell hybrid electric vehicle

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.

Automated summary

This paper presents a dual mode operation scheme for a range extend fuel cell hybrid vehicle, with a study of the pure electric mode and the range extend mode. A dual deep deterministic policy gradient algorithm framework is proposed to achieve optimal power distribution, along with a previous action guidance mechanism to improve learning convergence and exploration ability. The validation results show improved operating economy and reduced fuel cell lifetime loss.