Multi-variable optimization of PEMFC cathodes using an agglomerate model

A comprehensive numerical framework for cathode electrode design is presented and applied to predict the catalyst layer and the gas diffusion layer parameters that lead to an optimal electrode performance at different operating conditions. The design and optimization framework couples an agglomerate cathode catalyst layer model to a numerical gradient-based optimization algorithm. The set of optimal parameters is obtained by solving a multi-variable optimization problem. The parameters are the catalyst layer platinum loading, platinum to carbon ratio, amount of electrolyte in the agglomerate and the gas diffusion layer porosity. The results show that the optimal catalyst layer composition and gas diffusion layer porosity depend on operating conditions. At low current densities, performance is mainly improved by increasing platinum loading to values above 1 mg cm −2, moderate values of electrolyte volume fraction, 0.5, and low porosity, 0.1. At higher current densities, performance is improved by reducing the platinum loading to values below 0.35 mg cm −2 and increasing both electrolyte volume fraction, 0.55, and porosity 0.32. The underlying improvements due to the optimized compositions are analyzed in terms of the spatial distribution of the various overpotentials, and the effect of the agglomerate structure parameters (radius and electrolyte film) are investigated. The paper closes with a discussion of the optimized composition obtained in this study in the context of available experimental data. The analysis suggests that reducing the solid phase volume fraction inside the catalyst layer might lead to improved electrode performance.