Prediction and analysis of the cathode catalyst layer performance of proton exchange membrane fuel cells using artificial neural network and statistical methods

A mathematical model was developed to investigate the cathode catalyst layer (CL) performance of a proton exchange membrane fuel cell (PEMFC). A numerous parameters influencing the cathode CL performance are implemented into the CL agglomerate model, namely, saturation and eight structural parameters, i.e., ionomer film thickness covering the agglomerate, agglomerate radius, platinum and carbon loading, membrane content, gas diffusion layer penetration content and CL thickness. For the first time, an artificial neural network (ANN) approach along with statistical methods were employed for modeling, prediction, and analysis of the CL performance, which is denoted by activation overpotential. The ANN was constructed to build the relationship between the named parameters and activation overpotential. Statistical analysis, namely, analysis of means (ANOM) and analysis of variance (ANOVA) were done on the data obtained by the trained neural network and resulted in the sensitivity factors of structural parameters and their mutual combinations as well as the best performance.

Research highlights▶ For the first time, artificial neural network approach together with statistical methods (ANOM and ANOVA methods) are employed for modeling, prediction, and analysis of an agglomerate cathode catalyst layer (CL) performance. ▶ ANOM and ANOVA methods allow us extract physical explanations regarding the underlying system modeled using the ANN approach. ▶ Cathode CL thickness and the membrane volume content in CL were found to be the most significant structural parameters affecting the CL performance.