A Large-Scale Multi-Agent Deep Reinforcement Learning Method for Cooperative Output Voltage Control of PEMFCs

To increase the output voltage stability and improve the operating efficiency of proton exchange membrane fuel cells (PEMFCs), a data-driven cooperative method for controlling the PEMFC output voltage is proposed in this article. The proposed method adapts centralized learning and decentralized implementation, which can nonlinearly and adaptively realize optimal coordinated control over the hydrogen valve and the dc/dc converter. Additionally, a champion multiagent double delay deep deterministic policy gradient (CMA-4DPG) algorithm is proposed in this method, and the design of which incorporates the policies of the champion selection mechanism, cooperative exploration, imitation learning guidance, and curriculum guidance to improve the robustness of the cooperative method. The method cooperates with multiple controllers to prevent output voltage fluctuation. The experimental results show that by simultaneously regulating the hydrogen flow through the hydrogen valve and the duty ratio of the dc/dc converter, the proposed method achieves better robustness and can improve the tracking performance of the PEMFC output voltage.