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Automotive Engineering ›› 2023, Vol. 45 ›› Issue (3): 393-401.doi: 10.19562/j.chinasae.qcgc.2023.03.006

Special Issue: 新能源汽车技术-动力电池&燃料电池2023年

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Global Sensitivity Analysis of Multi-scale Parameters of the Fuel Cell Model Based on Two Cost Functions

Junfeng Wang,Jiqing Chen,Fengchong Lan(),Qingshan Liu,Changjing Zeng   

  1. 1.School of Mechanical & Automotive Engineering,South China University of Technology,Guangzhou  510640
    2.Guangdong Province Key Laboratory of Vehicle Engineering,Guangzhou  510640
  • Received:2022-10-21 Revised:2022-11-13 Online:2023-03-25 Published:2023-03-22
  • Contact: Fengchong Lan E-mail:fclan@scut.edu.cn

Abstract:

The semi-empirical model of the proton exchange membrane fuel cell has several unknown empirical parameters with widely varying scales and ranges of values, which can lead to biased results due to the loss of local high sensitivity information in the global sensitivity analysis. For this reason, double cost functions are constructed that are reciprocal to each other. On the basis of the original cost function, the correlation between the empirical parameters and the response error in the full domain is calculated. A first global sensitivity analysis is performed using the Sobol method for uniform sampling. The parameters with high sensitivity (i.e. affecting the speed of convergence) in the whole range of values are filtered; then the reciprocal cost function is used to amplify the error correlation in the local area, and for the remaining globally insensitive parameters, another global sensitivity analysis of the reciprocal cost function is carried out to filter the parameters with high sensitivity (i.e. affecting the accuracy of convergence) in the local range of values. Thus, the ability of identification of multi-scale and multi-local high-sensitivity parameters is improved. The analysis results show that the response error of the model after high-sensitivity parameter identification is consistent with the results of the full parameter identification, saving about 60% of the computational cost. The applicability and accuracy of the method are verified by fuel cell stack experiments.

Key words: PEMFC, semi-empirical model, global sensitivity analysis, cost function, parameter identification