A physics-based design rule for boosting PEMFC performances via gradient catalyst layer designs

To enhance cathode catalyst layer (CCL) performance of proton exchange membrane fuel cells (PEMFCs), this work proposes a rapid prediction of optimal structures for gradient cathode catalyst layers in diverse operating conditions. First, a one-dimensional agglomerate model is developed to quantifies how the CCL structural and operational parameters synergistically affect peak power density (P_max) and limiting current density (I_lim). Sensitivity analysis identifies relative humidity (RH) and air inlet pressure (p_in) as dominant factors governing PEMFC performance. A data-driven optimization model is then built to determine the optimal ionomer designs, which exhibit a unified dimensionless polarization curve independent of RH and p_in (within RH = 0.4–0.9, p_in = 1–2 atm). Leveraging this physics-based design rule, we propose a physics-based rapid prediction method to determine the optimal structures for both non-gradient and gradient CCL, under varying RH and p_in. Interestingly, the optimal ionomer content obtained by the single-objective optimization is almost identical to the multi-objective optimization results. Results demonstrate that the gradient CCL can improve P_max by >4 % and I_lim by ≈40 %. The insights in this work offers quantitative, practical guidance for robust gradient CCL design under variable conditions.