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Automotive Engineering ›› 2023, Vol. 45 ›› Issue (4): 627-636.doi: 10.19562/j.chinasae.qcgc.2023.04.011

Special Issue: 新能源汽车技术-电驱动&能量管理2023年

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A Supercapacitor SOC Estimation Method Based on Weighted Fusion Considering Ambient Temperature Variation

Chun Wang1,Tao Tang1,Yongzhi Zhang2()   

  1. 1.School of Mechanical Engineering,Sichuan University of Science and Engineering,Zigong  643000
    2.College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing  400044
  • Received:2022-10-23 Revised:2022-12-01 Online:2023-04-25 Published:2023-04-19
  • Contact: Yongzhi Zhang E-mail:yzzhangbit@gmail.com

Abstract:

Accurate estimation of the state of charge (SOC) of supercapacitors plays an important role in electric vehicle hybrid energy storage system, which directly determines the starting, climbing and accelerating performance of electric vehicles. Therefore, this paper proposes a supercapacitor SOC estimation method based on fuzzy entropy weighted fusion. Firstly, the Thevenin model parameters are identified by using the particle swarm algorithm under -10, 10, 25 and 40 ℃, and the nearest neighbor method is adopted to establish the mapping relation between the parameters and temperatures. Then, the fuzzy entropy is utilized to design a SOC weighted fusion estimation method based on three typical Kalman filters. Finally, the SOC estimation method of adaptive weighted averaging and residual normalized weighted fusion is selected to further evaluate the performance of the proposed method in this paper. The results show that supercapacitor SOC estimation method based on fuzzy entropy weighted fusion can improve the supercapacitor SOC estimation accuracy, especially in high ambient temperature environment (40 ℃) .

Key words: supercapacitor, state of charge, variable temperature model, Kalman filter, fusion estimation