Open AccessEffective Parameterization of PEM Fuel Cell Models—Part II: Robust Parameter Subset Selection, Robust Optimal Experimental Design, and Multi-Step Parameter Identification Algorithm
Author(s) -
Alireza Goshtasbi,
Jixin Chen,
James Waldecker,
Shinichi Hirano,
Tulga Ersal
Publication year - 2020
Publication title -
journal of the electrochemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.258
H-Index - 271
eISSN - 1945-7111
pISSN - 0013-4651
DOI - 10.1149/1945-7111/ab7092
Subject(s) - identification (biology) , sensitivity (control systems) , algorithm , selection (genetic algorithm) , estimation theory , mathematical optimization , optimal design , system identification , computer science , design of experiments , parameter identification problem , model parameter , mathematics , data mining , engineering , machine learning , statistics , botany , biology , electronic engineering , measure (data warehouse)
The second part of this two-part study develops a systematic framework for parameter identification in polymer electrolyte membrane (PEM) fuel cell models. The framework utilizes the extended local sensitivity results of the first part to find an optimal subset of parameters for identification. This is achieved through an optimization algorithm that maximizes the well-known D-optimality criterion. The sensitivity data are then used for optimal experimental design (OED) to ensure that the resulting experiments are maximally informative for the purpose of parameter identification. To make the experimental design problem computationally tractable, the optimal experiments are chosen from a predefined library of operating conditions. Finally, a multi-step identification algorithm is proposed to formulate a regularized and well-conditioned optimization problem. The identification algorithm utilizes the unique structure of output predictions, wherein sensitivities to parameter perturbations typically vary with the load. To verify each component of the framework, synthetic experimental data generated with the model using nominal parameter values are used in an identification case study. The results confirm that each of these components plays a critical role in successful parameter identification.