In order to improve the operational efficiency and stability of proton exchange membrane fuel cell (PEMFC), a distributed deep reinforcement learning (DDRL)-based integrated control strategy is proposed to solve the coordinated control problem of water pump and radiator in stack heat management system. This strategy substitutes the independent controllers of the water pump and radiator in the traditional control framework, and employs multi-input multi-output (MIMO) agents which simultaneously control the cooling water velocity of the water pump and the air velocity of the radiator, whilst monitoring the optimal global stack temperature control performance. To this end, an efficient curriculum exploration distributed double-delay deep determinate policy gradient (ECE-5DPG) algorithm is proposed for the strategy, the design of which is based on the concepts of curriculum learning, imitation learning, and distributed exploration, thus improving the robustness of the proposed strategy. The experimental results show that the proposed integrated control strategy can effectively control the cooling water velocity and air velocity simultaneously, thereby improving the operating efficiency of the PEMFC.

为了提高质子交换膜燃料电池（PEMFC）的运行效率和稳定性，提出了一种基于分布式深度强化学习（DDRL）的集成控制策略来解决电堆热管理系统中水泵和散热器的协调控制问题。 . 该策略取代了传统控制框架中水泵和散热器的独立控制器，采用多输入多输出（MIMO）代理，同时控制水泵的冷却水流速和散热器的空气流速，同时监控最佳的全局烟囱温度控制性能。为此，针对该策略提出了一种高效的课程探索分布式双延迟深度确定策略梯度（ECE-5DPG）算法，其设计基于课程学习、模仿学习和分布式探索的概念，从而提高了所提出策略的鲁棒性。实验结果表明，所提出的集成控制策略可以有效地同时控制冷却水流速和空气流速，从而提高PEMFC的运行效率。