On Parameterizing PEM Fuel Cell Models

A methodology for parameterizing polymer electrolyte membrane (PEM) fuel cell models is presented. The procedure starts by optimal experimental design (OED) for parameter identification. This is done by exploring output sensitivities to parameter variations in the space of operating conditions. Once the optimal operating conditions are determined, they are used to gather synthetic experimental data. The synthetic data are then used to identify 7 model parameters in a step-by-step procedure that involves grouping the parameters for identification based on the preceding sensitivity analysis. Starting from the kinetic region of the polarization curve and continuing with the ohmic and mass transport regions, the parameters are identified in a cumulative fashion using a gradient-based nonlinear least squares algorithm. The impact of the OED for parameter identification is explored by comparing the results with another set of synthetic data obtained by Latin Hypercube Sampling (LHS) of the operating space. The results indicate improved identification with OED compared to LHS and point to the utility of the systematic approach, presented herein, for identifying the parameters of PEM fuel cell models.