Paper Info
Title
Robust Parameter Subset Selection and Optimal Experimental Design for Effective Parameterization of PEM Fuel Cell Models
Abstract
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.
Year
DOI
Venue
2020
10.23919/ACC45564.2020.9147213
2020 American Control Conference (ACC)
Keywords
DocType
ISSN
Fuel cells,Mathematical model,Computational modeling,Analytical models,Robustness,Sensitivity analysis
Conference
0743-1619
ISBN
Citations 
PageRank 
978-1-5386-8266-1
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Alireza Goshtasbi100.34
Jixin Chen200.34
James R. Waldecker300.34
Shinichi Hirano400.34
Tulga Ersal53315.63