Fast design of catalyst layer with optimal electrical-thermal-water performance for proton exchange membrane fuel cells

The catalyst layer(CL)is the core component in determining the electrical-thermal-water performance and cost of proton exchange membrane fuel cell(PEMFC).Systemic analysis and rapid prediction tools are required to improve the design efficiency of CL.In this study,a 3D multi-phase model integrated with the multi-level agglomerate model for CL is developed to describe the heat and mass transfer processes inside PEMFC.Moreover,a research framework combining the response surface method(RSM)and artificial neural network(ANN)model is proposed to conduct a quantitative analysis,and further a rapid and accurate prediction.With the help of this research framework,the effects of CL composition on the electrical-thermal-water performance of PEMFC are investigated.The results show that the mass of platinum,the mass of carbon,and the volume fraction of dry ionomer has a significant impact on the electrical-thermal-water performance.At the selected points,the sensitivity of the decision variables is ranked:volume fraction of dry ionomer>mass of platinum>mass of carbon>agglomerate radius.In particular,the sensitivity of the volume fraction of dry ionomer is over 50%at these points.Besides,the comparison results show that the ANN model could implement a more rapid and accurate prediction than the RSM model based on the same sample set.This in-depth study is beneficial to provide feasible guidance for high-performance CL design.