Elucidating Degradation Mechanisms through Modeling of Proton-Exchange Membrane Electrolytic Cells

Proton exchange membrane (PEM) electrolytic cells (ECs) have gained significant importance in the realm of hydrogen production. With the increasing demand for commercializing PEMECs, there is a pressing need for enhancing their performance and durability. In addition to experiments, macro-scale modeling plays a pivotal role in comprehending the multi-physics processes within PEMECs. This presentation highlights the development of physics-based and data-based models tailored for PEMECs. They serve as the foundation for elucidating degradation mechanisms of PEMECs, aiming to prolong their lifespan and improve their efficiency for a quick technology ramp up. These models explore the electrochemical processes and transport phenomena occurring within the porous transport layers (PTLs), catalyst layers (CLs), and the PEM. The high-aspect-ratio PEMECs are well-suited for one-dimensional (1D) analysis [1-3]. The 1D analysis is implemented in Python, with COMSOL also utilized for 2D and 3D simulations. Unfortunately, the complex nature of PEMECs as thermal, fluidic, and electrochemical systems poses challenges in accurate diagnostics. Modeling these cells involves addressing uncertainties in operational conditions, material properties, and design features. Additionally, uncertainties in experimental data, model selection, parameters, and model inadequacies must be carefully considered during PEMEC modeling. While uncertainty quantification (UQ) and sensitivity analysis (SA) can unveil how uncertainties propagate from input parameters to the output of PEMEC models, UQ has not been extensively integrated into PEMEC modeling. Finally, this presentation underscores the potential benefits of physics-based modeling combined with UQ and SA for PEM electrolysis [3]. Acknowledgment: Financial support was provided by the German Federal Ministry of Education and Research (BMBF) within the H2Giga project DERIEL (grant number 03HY122C). Literature: [1] P. Trinke, 2021. [2] P. A. García-Salaberri, J. Power Sources, 2022, 521. [3] V. Karyofylli, Y. Danner, K. Ashoke Raman, H. Kungl, A. Karl, E. Jodat, R.-A. Eichel, J. Power Sources, 2024, 600, 234209.