Machine learning-assisted design of flow fields for proton exchange membrane fuel cells

Optimizing the flow field is a key approach to enhancing the performance of proton exchange membrane fuel cells. Most previous research on flow field design relies on physical intuitions, lacking systematic exploration of the complex geometries of flow fields. To address these issues, we first generate a comprehensive flow field library containing 28,348 geometries using the Depth-First Search algorithm. Subsequently, we randomly select 480 flow fields from this library and characterize their corresponding fuel cell performance using computational fluid dynamics . These 480 flow fields serve as the dataset for machine learning training. Using the trained neural network , we rapidly predict fuel cell performance and identify high-performance flow fields. Simulation results demonstrate that the predicted high-performance flow fields effectively improve mass transfer and current density distribution, thereby enhancing current density and maximum power density . Experimental validation shows a 10.37 % increase in maximum power density for our optimized flow field design compared to traditional serpentine channels. Additionally, our geometric analysis identifies key features of high-performance flow fields, guiding future designs. • Machine learning optimizes PEMFC flow fields for enhanced performance. • Depth-first search generates a library of 28,348 flow field geometries. • Neural network predicts fuel cell performance from CFD-characterized data. • Optimized flow fields improve mass transfer and current density distribution. • Experimental validation shows a 10.37 % increase in maximum power density.