Modeling membrane degradation in PEM electrolyzers with Physics-Informed Neural Networks

A. Polo-Molina, J. Portela, L.A. Herrero, R. Cicero González

Energy and AI Vol. 26, pp. 100930

Summary:

Proton exchange membrane (PEM) electrolyzers are pivotal for sustainable hydrogen production, yet their long-term performance is hindered by membrane degradation, which poses reliability and safety challenges. Therefore, accurate modeling of this degradation is essential for optimizing durability and performance. To address these concerns, traditional physics-based models have been developed, offering interpretability but requiring numerous parameters that are often difficult to measure and calibrate. Conversely, data-driven approaches, such as machine learning, offer flexibility but may lack physical consistency and generalizability. To address these limitations, this study presents the first application of Physics-Informed Neural Networks (PINNs) to model membrane degradation through membrane thinning in PEM electrolyzers. The proposed PINN framework couples two ordinary differential equations, one modeling membrane thinning via a first-order degradation law and another governing the time evolution of the cell voltage under membrane degradation. Results demonstrate that the PINN accurately captures the long-term system’s degradation dynamics while preserving physical interpretability with limited noisy data. Consequently, this work introduces a novel hybrid modeling approach for estimating and understanding membrane degradation mechanisms in PEM electrolyzers, offering a foundation for more robust predictive tools in electrochemical system diagnostics.

Spanish layman's summary:

Desarrollamos un modelo de inteligencia artificial con conocimiento físico integrado para predecir el envejecimiento de membranas en electrolizadores de hidrógeno PEM. La herramienta permite estimar su vida útil y mejorar la seguridad y fiabilidad de la producción de hidrógeno renovable.

English layman's summary:

We developed a physics-informed artificial intelligence model to predict membrane ageing in PEM hydrogen electrolyzers. The tool estimates component lifetime and improves the safety and reliability of renewable hydrogen production using limited operational data.

Keywords: Physics-Informed Neural Networks; PEM electrolyzers; PEM modelling; Membrane degradation modelling; Machine learning

JCR-JIF Impact Factor and WoS quartile: 9,700 - Q1 (2025)

DOI reference: https://doi.org/10.1016/j.egyai.2026.100930

Published on paper: December 2026.

Published on-line: October 2026.

Citation:
A. Polo-Molina, J. Portela, L.A. Herrero, R. Cicero González, "Modeling membrane degradation in PEM electrolyzers with Physics-Informed Neural Networks", Energy and AI, Vol. 26, pp. 100930, December 2026. [Online: October 2026] doi: 10.1016/j.egyai.2026.100930