Robust Parameter Subset Selection and Optimal Experimental Design for Effective Parameterization of PEM Fuel Cell Models

We address the problem of identifying the parameters of a polymer electrolyte membrane (PEM) fuel cell model when no prior parameter estimates are available. To this end, we build upon a recently developed parameter identification framework and use an extended local sensitivity analysis to obtain a more global picture of parameter sensitivities. The extended analysis consists of local analyses carried out at multiple sampled points in the parameter space. The results from this extended analysis are then used to optimally select a subset of parameters for identification. Particularly, the selected subset optimizes the expected value of the well-known D-criterion over the parameter space. Being derived from the extended analysis, the selected subset is robust to initial assumptions about the nominal parameter values. Similar procedures are then used for robust optimal experimental design (OED) for the purpose of parameter identification. The robust OED approach is benchmarked against another experimental design method based on Latin Hypercube Sampling (LHS). The effectiveness of the proposed methods is investigated by identifying model parameters using synthetic data. The results demonstrate the utility of the robust parameter subset selection and OED procedures in enabling accurate parameter identification.