Thermal analysis of fuel cells in renewable energy systems using Generative Adversarial Networks (GANs) and Reinforcement learning

This research introduces a novel in silico thermal management strategy for fuel cell systems that combines Generative Adversarial Networks (GANs) and Reinforcement Learning (RL). The framework utilises the unconditional generation ability of GANs to produce realistic thermal management, which can be used to train an RL agent to control system parameters dynamically. A GAN can be trained on historical data or simulation results to generate realistic and diverse scenarios of the complex relationship between the working point and the thermodynamics of a fuel cell. Once the adversarial network is trained to output realistic data, it can feed an RL agent that tunes the parameters influencing cooling, such as coolant flow rates, air stoichiometry or reactant humidification in response to a generated working point. We conduct experiments and simulations to show that GANs can improve the performance and resilience of RL agents to uncertainty. This approach can be applied to a realworld system, demonstrating significant thermal efficiency, durability and robustness improvements. Adopting such a strategy for thermal management in a fuel cell system is just one example of a new class of intelligent, data-driven control approaches that will enable improved understanding and behaviour of systems, leading to better performance and reliability.