To compensate for the slow dynamic response of fuel cell system (FCS) in fuel cell hybrid electric vehicles (FCHEVs), battery and supercapacitor are widely used as auxiliary supply energy sources in FCHEVs. How to reasonably allocate the required power among the three energy sources, reduce the fuel consumption and the SOC fluctuations of auxiliary energy sources becomes a tough problem to be solved. In this paper, we propose an energy management strategy (EMS) based on fuzzy control, considering the multiple constraints of hydrogen consumption and auxiliary energy SOC fluctuations of FCHEV. In this paper, the ant colony algorithm (ACA) is used for the first time to optimize the membership functions parameters of the dual fuzzy controller. The results obtained are verified in Matlab/Simulink. The proposed ACA optimized EMS can effectively reduce the equivalent hydrogen consumption and the SOC fluctuation of battery and supercapacitor by proving in four typical driving conditions.

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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.