Multiscale Simulation in Fuel Cell and Electrolyzer Systems: A Review of Methods, Applications, and Future Prospects

This review explores the transformative role of multiscale modeling in advancing fuel cell and electrolyzer technologies, which are essential for achieving decarbonized energy systems. By integrating quantum-level reaction mechanisms, mesoscale transport phenomena, and macroscopic system dynamics, it establishes a cohesive framework for optimizing electrochemical device performance. Key theoretical advances are discussed, including hybrid quantum-mechanics/continuum approaches that capture ionic interactions at atomic resolution, and machine learning-enhanced models that accurately predict microstructural evolution. The review highlights how AI-driven multiscale simulations simultaneously reduce computational demands and enhance predictive power, particularly in assessing material degradation and performance thresholds. Importantly, this work bridges the traditional divide between electrochemical modeling and data science, paving the way for digital twin technologies. By addressing challenges in scale coupling and model validation, this study accelerates the path toward commercial development of high-efficiency hydrogen technologies. These findings are especially relevant for industries pursuing net-zero targets through advanced energy storage solutions, providing both methodological innovations and practical guidance for next-generation fuel cell design.