Efficient Prediction of Fuel Cell Performance Using Global Modeling Method

Qinwen Yang, Gang Xiao (), Tao Liu, Bin Gao and Shujun Chen
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Qinwen Yang: College of Mechanical and Vehicle Engineering, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
Gang Xiao: College of Mechanical and Vehicle Engineering, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
Tao Liu: College of Mechanical and Vehicle Engineering, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
Bin Gao: College of Mechanical and Vehicle Engineering, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
Shujun Chen: College of Mechanical and Vehicle Engineering, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China

Energies, 2022, vol. 15, issue 22, 1-14

Abstract: A global modeling method is developed to describe the relationship between multi-type parameters and fuel cell performance, which significantly contributes to the efficient performance prediction of fuel cell systems. The multi-type parameters include operating parameters, geometric parameters of the graphite end plates, and the membrane electrolyte assembly physical parameters. An adaptive sampling method integrated with the Kriging method is newly developed for global modeling. Experiments are designed and implemented for model construction and evaluation. The results show the local development and global development in the whole design space can be balanced during the adaptive sampling process. Meanwhile, the prediction capability of accuracy and sensitivity for the global model is reliable in the whole design space. The prediction accuracy is improved by nearly 26% compared to the fuel cell model built for optimization with the same sample size. The prediction sensitivity also proved that the global model could follow the experimental variations under small input deviations.

Keywords: fuel cell; energy conversion efficiency; global modeling method; adaptive sampling method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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