Effective energy management strategy based on deep reinforcement learning for fuel cell hybrid vehicle considering multiple performance of integrated energy system
Haoqin Hu, Chenlei Lu, Jiaqi Tan, Shengnan Liu, Dongji Xuan
Abstract
To reduce the power burden of the fuel cell system (FCS) in fuel cell hybrid electric vehicles (FCHEVs), battery and supercapacitor are widely used as energy storage system (ESS) in FCHEVs. In this paper, an energy management strategy (EMS) based on deep reinforcement learning is proposed to solve the power allocation problem among three energy sources. Considering that three energy sources increase the complexity of the state action space, this paper proposes an adaptive fuzzy control filter to separate the load demand power to reduce the computational burden of the deep deterministic policy gradient (DDPG) algorithm. Meanwhile, a learning guidance mechanism (LGM) is proposed in this paper to guide the agent in a rational learning direction, and the reward convergence results are presented in this paper. The training results show that the equivalent hydrogen consumption reaches 99.5% of fuzzy logic controlled-based EMS (FLC-EMS), the FCS efficiency improves by 5.9%, and the stability of battery and supercapacitor is also significantly improved. Eventually, three different driving conditions are selected to verify the effectiveness of the proposed EMS, the results show that the proposed EMS in this paper outperforms (FLC-EMS) in all aspects.