As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7 % .

中文翻译：

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使用深度强化学习和动作修剪的燃料电池/电池/超级电容器混合动力电动汽车的能源管理策略


对于配备电池（BAT）和超级电容器（UC）的燃料电池混合动力汽车来说，其动态拓扑结构复杂，三种电源的不同特性给能量管理带来了燃油经济性、电源寿命和动态性能方面的挑战。车辆。本文提出了一种基于分层功率分割结构和深度强化学习（DRL）的能量管理策略（EMS）。在所提出的EMS的高层策略中，UC用于提供峰值功率并通过基于模糊控制的自适应滤波器回收制动能量。然后，提出集成DRL和等效消耗最小化策略框架，优化下层燃料电池（FC）和BAT的功率分配，保证FC高效运行并降低氢消耗。并提出基于启发式技术的动作调整，以进一步抑制突发峰值功率对FC寿命的不利影响。仿真结果表明，所提出的EMS可以使FC的输出更加平滑，提高其工作效率，缓解BAT的压力，在WLTP工况下燃油经济性较Q-learning策略提高14.8%。同时，在UDDSHDV下获得的结果表明，所提出的EMS的燃油经济性可以达到动态规划（DP）基准水平89.7%。