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 (Pmax) and limiting current density (Ilim). Sensitivity analysis identifies relative humidity (RH) and air inlet pressure (pin) 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 pin (within RH = 0.4–0.9, pin = 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 pin. 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 Pmax by >4 % and Ilim by ≈40 %. The insights in this work offers quantitative, practical guidance for robust gradient CCL design under variable conditions.