Advancing next-generation proton exchange membrane fuel cell design through multi-physics and AI modeling

Abstract

Next-generation proton exchange membrane (PEM) fuel cells of high power density and durability are a cornerstone technology for future sustainable energy systems. While traditional three-dimensional (3D) full-size computational fluid dynamics (CFD) modeling has been pivotal in fuel cell design by numerically resolving electrochemically coupled multi-physics transfers, it faces persistent challenges, including a major theoretical gap in channel two-phase flow physics, oversimplified representations of catalyst layer (CL) microstructures, outdated membrane correlations, inadequate validation, and lack of consideration on material degradation. This perspective paper identifies key challenges and opportunities for fuel cell design through multi-physics and artificial intelligence (AI) modeling. Data-driven sub-models describing specific physics (e.g., multi-physics transfers within CLs) can be integrated with traditional modeling frameworks to balance the trade-off between computational efficiency and accuracy. Moreover, by utilizing physics-informed operator learning (e.g., PI-DeepONet) for evolution of multi-physics distributions and generating datasets via transient 3D models incorporating balance of plant (BOP) components, it is foreseeable to ultimately pave the way for predictive digital twins enabling health monitoring and degradation analysis throughout the life cycle, crucial for developing next-generation high-power-density and durable PEM fuel cells.