Modeling Membrane Degradation in PEM Electrolyzers with Physics-Informed Neural Networks

• URL: http://arxiv.org/abs/2507.02887v1
• Date: Thu, 19 Jun 2025 15:46:49 GMT
• Title: Modeling Membrane Degradation in PEM Electrolyzers with Physics-Informed Neural Networks
• Authors: Alejandro Polo-Molina, Jose Portela, Luis Alberto Herrero Rozas, Román Cicero González,
• Abstract summary: Proton exchange membrane (PEM) electrolyzers are pivotal for sustainable hydrogen production.<n>Their long-term performance is hindered by membrane degradation, which poses reliability and safety challenges.<n>Traditional physics-based models have been developed, offering interpretability but requiring numerous parameters that are often difficult to measure and calibrate.<n>This study presents the first application of Physics-Informed Neural Networks (PINNs) to model membrane degradation in PEM electrolyzers.
• Score: 45.32169712547367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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.

Related papers

• PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes [0.8376229126363229]
  PINEAPPLE is a novel framework that integrates physics-informed neural networks (PINNs) with an evolutionary search algorithm to enable rapid, scalable, and interpretable parameter inference.<n> PINEAPPLE demonstrates robust parameter inference solely from voltage-time discharge curves across multiple batteries from the open-source CALCE repository.<n>By enabling computationally efficient, real-time parameter estimation, PINEAPPLE offers a promising route towards the non-destructive, physics-based characterization of inter-cell and intra-cell variability.
  arXiv  Detail & Related papers  (2026-02-20T07:51:59Z)
• Physics-Informed Neural Operators for Cardiac Electrophysiology [2.789396703574285]
  We propose a Physics-Informed Neural Operator (PINO) approach to solve PDE problems in cardiac electrophysiology.<n>Our results show that PINO models can accurately reproduce cardiac EP dynamics over extended time horizons and across multiple propagation scenarios.<n>These advantages come with a significant reduction in simulation time compared to numerical PDE solvers.
  arXiv  Detail & Related papers  (2025-11-11T16:28:56Z)
• Temperature-Aware Recurrent Neural Operator for Temperature-Dependent Anisotropic Plasticity in HCP Materials [1.2374932078540024]
  We introduce the Temperature-Aware Recurrent Neural Operator (TRNO), a time-resolution-independent neural architecture.<n>The TRNO achieves high predictive accuracy and generalizes effectively across diverse loading cases, temperatures, and time resolutions.<n>It also outperforms conventional GRU and LSTM models in training efficiency and predictive performance.
  arXiv  Detail & Related papers  (2025-08-26T08:40:42Z)
• Physics-Embedded Neural ODEs for Sim2Real Edge Digital Twins of Hybrid Power Electronics Systems [6.073480880825787]
  Edge Digital Twins (EDTs) are crucial for monitoring and control of Power Electronics Systems (PES)<n>Existing modeling approaches struggle to consistently capture continuously evolving hybrid dynamics that are inherent in PES.<n>This paper proposes a Physics-Embedded Neural ODEs (PENODE) that embeds the hybrid operating mechanism as an event automaton to explicitly govern switching discrete.
  arXiv  Detail & Related papers  (2025-08-04T20:34:13Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• Physics-Embedded Neural Networks for sEMG-based Continuous Motion Estimation [3.606446851103922]
  sEMG-based motion estimation methods often rely on subject-specific musculoskeletal (MSK) models that are difficult to calibrate.<n>This paper introduces a novel Physics-Embedded Neural Network (PENN) that combines interpretable MSK forward-dynamics with data-driven residual learning.
  arXiv  Detail & Related papers  (2025-06-17T16:07:20Z)
• Spatiotemporal Field Generation Based on Hybrid Mamba-Transformer with Physics-informed Fine-tuning [46.67399627400437]
  This research confronts the challenge of substantial physical equation discrepancies in the generation of physical fields through trained models.<n>A physical field generation model, named HMT-PF, is developed based on the hybrid Mamba-Transformer architecture.
  arXiv  Detail & Related papers  (2025-05-16T14:40:56Z)
• BPINN-EM-Post: Stochastic Electromigration Damage Analysis in the Post-Void Phase based on Bayesian Physics-Informed Neural Network [2.228763526924048]
  We introduce a novel machine learning-based framework, termed BPINN-EM-Post, for efficient analysis of EM-induced postvoiding aging processes.<n>By reducing the number of variables in the loss functions through the use of analytical solutions, our method significantly improves training efficiency without accuracy loss and naturally incorporates variational effects.
  arXiv  Detail & Related papers  (2025-03-18T00:31:12Z)
• ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning [0.0]
  This work establishes a foundational battery degradation model, providing reliable forecasts across a range of battery types and operating conditions.<n>Our novel framework uses contrastive learning to map the relationship between the underlying physical degradation parameters and observable operational quantities.
  arXiv  Detail & Related papers  (2025-01-17T12:13:04Z)
• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  We propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential.<n>Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations.<n>Our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and is at least 3,000 times faster compared to first-principles molecular dynamics.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries [8.929862063890974]
  We propose a model fusion scheme based on Physics-Informed Neural Network (PINN) It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion batteries. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework.
  arXiv  Detail & Related papers  (2023-01-02T17:51:23Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.