Application of Machine Learning in Optimizing Proton Exchange Membrane Fuel Cells: A Review

Abstract

Proton exchange membrane fuel cells (PEMFCs) as energy conversion devices for hydrogen energy are crucial for achieving an eco-friendly society, but their cost and performance are still not satisfactory for large-scale commercialization. Multiple physical and chemical coupling processes occur simultaneously at different scales in PEMFCs. Hence, previous studies only focused on the optimization of different components in such a complex system separately. In addition, the traditional trial-and-error method is very inefficient for achieving the performance breakthrough goal. Machine learning (ML) is a tool from the data science field. Trained based on datasets built from experimental records or theoretical simulation models, ML models can mine patterns that are difficult to draw intuitively. ML models can greatly reduce the cost of experimental attempts by predicting the target output. Serving as surrogate models, the ML approach could also greatly reduce the computational cost of numerical simulations such as first-principle or multiphysics simulations. Related reports are currently trending, and ML has been proven able to speed up tasks in this field, such as predicting active electrocatalysts, optimizing membrane electrode assembly (MEA), designing efficient flow channels, and providing stack operation strategies. Therefore, this paper reviews the applications and contributions of ML aiming at optimizing PEMFC performance regarding its potential to bring a research paradigm revolution. In addition to introducing and summarizing information for newcomers who are interested in this emerging cross-cutting field, we also look forward to and propose several directions for future development.