Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale, two-phase fuel cell model and data-driven surrogates

Polymer electrolyte membrane fuel cells (PEMFCs) are considered a promising alternative to internal combustion engines in the automotive sector. Their commercialization is mainly hindered due to the cost and effectiveness of using platinum (Pt) in them. The cathode catalyst layer (CL) is considered a core component in PEMFCs, and its composition often considerably affects the cell performance (V_cell) also PEMFC fabrication and production C_stack costs. In this study, a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on V_cell and C_stack. Four essential cathode CL parameters, i.e., platinum loading (L_Pt), weight ratio of ionomer to carbon (wt_I/C), weight ratio of Pt to carbon (wt_Pt/c), and porosity of cathode CL (ε_cCL), are considered as the design variables. The simulation results of a three-dimensional, multi-scale, two-phase comprehensive PEMFC model are used to train and test two famous surrogates: multi-layer perceptron (MLP) and response surface analysis (RSA). Their accuracies are verified using root mean square error and adjusted R². MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithm II. Compared to a typical PEMFC stack, the results of the optimal study show that the single-cell voltage, V_cell is improved by 28 mV for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by $5.86/kW for the same stack performance.