Multi-objective optimization of PEMFC performance based on grey correlation analysis and response surface method

Abstract

This paper aims to combine grey correlation analysis and response surface method to propose a fast and effective performance optimization method for PEMFC. First, based on orthogonal test data, grey correlation analysis method is used to select four variables that have significant influence on PEMFC’s comprehensive performance from eight common parameters. Secondly, based on grey correlation analysis, the multi-objective optimization problem is transformed into a single objective optimization problem about correlation degree, and applying the response surface method to build the key parameters and the correlation between the second order prediction model. Therefore, the current density, system efficiency and oxygen distribution uniformity on cathode catalyst layer of PEMFC were optimized as a whole. Finally, the optimal parameter combination was obtained by optimizing the prediction model. The simulation results show that the optimized operating conditions are significantly improved in the three performance indexes compared with the basic model, which confirms the feasibility of this method in solving the multi-objective optimization problem, and can provide some reference for the optimal design of hydrogen fuel cells.