The two power sources of a fuel cell electric vehicle (FCEV) are proton electrolyte membrane fuel cell (PEMFC) and Li‐ion battery (LIB). The health status of PEMFC and LIB decreases with the use of FCEV, so the energy management strategy (EMS) needs to give an optimal power distribution based on the health status of power sources throughout the lifetime. However, rule‐based control strategies cannot achieve this. To prolong the service lifetime of two power sources by optimizing power distribution, this article proposes a long‐term energy management strategy (LTEMS) for FCEV, which contains a reinforcement learning module and an improved thermostat controller. By designing a reward function, the reinforcement learning module outputted various LIB state of charge (SOC) boundary which changes with power source attenuation. Based on SOC boundary, the improved thermostat controller will control the fuel cell current under specific driving conditions. Simulation was carried out based on different LIB state of health (SOH) and external temperature, and the simulation results were compared with the data collected from FCEV under rule‐based (RB) strategies. It can be found that the proposed LTEMS can effectively reduce fuel cell and LIB attenuation, and meet the FCEV power demand.

中文翻译：

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使用强化学习的燃料电池电动汽车长期能源管理策略

燃料电池电动汽车（FCEV）的两种动力来源是质子电解质膜燃料电池（PEMFC）和锂离子电池（LIB）。PEFC和LIB的健康状况会随着FCEV的使用而降低，因此，能源管理策略（EMS）需要根据整个生命周期中电源的健康状况给出最佳的功率分配。但是，基于规则的控制策略无法实现这一目标。为了通过优化配电来延长两个电源的使用寿命，本文提出了一种用于FCEV的长期能源管理策略（LTEMS），该策略包含强化学习模块和改进的恒温控制器。通过设计奖励功能，强化学习模块输出随电源衰减而变化的各种LIB充电状态（SOC）边界。根据SOC边界 改进的恒温器控制器将在特定驾驶条件下控制燃料电池电流。根据不同的LIB健康状况（SOH）和外部温度进行了仿真，并将仿真结果与基于规则（RB）策略从FCEV收集的数据进行了比较。可以发现，提出的LTEMS可以有效地减少燃料电池和LIB的衰减，并满足FCEV功率需求。