Water management scheme based on prioritized deep deterministic policy gradient for proton exchange membrane fuel cells

To effectively tackle the intricate and dynamic challenges encountered in proton exchange membrane fuel cells (PEMFCs), this paper introduces a model-free reinforcement learning approach to address its water management issue. Recognizing the limitations of conventional reinforcement learning methods such as Q-learning in handling the continuous actions and nonlinearity inherent in PEMFCs water management, we propose a prioritized deep deterministic policy gradient (DDPG) method. This method, rooted in the Actor-Critic framework, leverages double neural networks and prioritized experience replay to enable adaptive water management and balance. Additionally, we establish a PEMFCs water management platform and implement the prioritized DDPG method using "Tianshou", a modularized Python library for deep reinforcement learning. Through experimentation, the effectiveness of our proposed method is verified. This study contributes to advancing the understanding and management of water dynamics in PEMFCs, offering a promising avenue for enhancing their performance and reliability. Article Highlights Enhanced water management: Prioritized DDPG method optimizes water balance in PEMFCs, addressing dynamic conditions. Actor-Critic framework: Double neural networks and prioritized experience replay ensure adaptive water management. Practical implementation: Proposed method validated on a PEMFCs platform, confirming its effectiveness in real-world scenarios.