Abstract
This study introduces the first application of Physics-Informed Neural Networks (PINNs) to model membrane degradation in proton exchange membrane (PEM) electrolyzers, which are essential for sustainable hydrogen production. Traditional physics-based models offer physical interpretability but rely on numerous parameters that are difficult to measure, while data-driven models like machine learning provide flexibility but often lack generalizability and consistency with physical laws. The proposed PINN framework bridges this gap by integrating two ordinary differential equations: one describing membrane thinning through a first-order degradation law, and another modeling the time evolution of cell voltage due to degradation. The results show that the PINN effectively captures long-term degradation dynamics using limited and noisy data, while preserving physical meaning. This hybrid modeling approach provides a robust and accurate tool for understanding and predicting membrane degradation in PEM electrolyzers. It offers a promising foundation for improved diagnostics and performance optimization in electrochemical systems subjected to aging and reliability challenges.
Modeling Membrane Degradation in PEM Electrolyzers with Physics-Informed Neural Networks
