Polymer electrolyte membrane fuel cell performance Revolutionized: Artificial intelligence-validated asymmetric flow channels enhance mass transport via hybrid analytical-numerical frameworks

The enhancement of the flow channel design of polymer electrolyte membrane fuel cells (PEMFCs) is imperative for the improvement of mass transport and overall performance. This study introduces novel asymmetric gas channel cross-sectional profiles, validated through a tripartite approach encompassing analytical modeling, numerical simulations, and experimental testing. The proposed profiles are subjected to analytical examination through the implementation of a combination of the regular perturbation method and the Galerkin approach to efficiently solve nonlinear governing equations. Four innovative designs (c1 to c4) are evaluated, and the results consistently demonstrate that the c3 configuration with a cross-section parameter ε = 0.5 achieves superior performance by optimizing species transport and reducing concentration losses. Experimental validation confirms a current density improvement of up to 5.6 % over conventional designs, while Artificial Intelligence (AI)-driven optimization via a hybrid Convolutional Neural Network and Genetic Algorithm independently identifies the same optimal configuration. The preponderance of evidence from analytical, numerical, experimental, and AI-driven methods corroborates the efficacy of the proposed design as a resilient and expandable solution for enhancing PEMFC efficiency.