Hyper-Efficient Proton Exchange Membrane Fuel Cell Stack Optimization via Dynamic Reconfiguration using Reinforcement Learning and Multi-Modal Data Fusion

**Abstract:** This paper details a novel approach to optimize the performance and longevity of Proton Exchange Membrane (PEM) Fuel Cell Stacks through dynamic reconfiguration driven by a reinforcement learning (RL) agent. Utilizing a multi-modal data fusion paradigm incorporating flow field imaging, electrochemical impedance spectroscopy (EIS), and temperature mapping, the RL agent learns to dynamically adjust stack operating parameters in real-time, maximizing power density while minimizing degradation factors. The proposed system demonstrates a potential 15-20% improvement in efficiency and a corresponding extension of fuel cell lifespan, presenting a commercially viable solution for enhanced PEM fuel cell performance.