A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles

Bo Huang, Yuting Ma, Chun Wang, Yongzhi Chen and Quanqing Yu
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Bo Huang: School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
Yuting Ma: Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China
Chun Wang: School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
Yongzhi Chen: School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
Quanqing Yu: School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China

Energies, 2021, vol. 14, issue 15, 1-16

Abstract: The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE can be correspondingly reduced by 60.41%, 47.26%, 23.04%. The results indicate that the two-layer fusion model proposed in this paper achieves better robustness and accuracy.

Keywords: supercapacitor; parameter identification; genetic algorithm; fusion model (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: 2021
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