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A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty

Author

Listed:
  • Chun Wang

    (School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
    School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Chaocheng Fang

    (School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China)

  • Aihua Tang

    (School of Vehicle Engineering, Chongqing University of Technology, Chongqing 400054, China)

  • Bo Huang

    (School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
    Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China)

  • Zhigang Zhang

    (School of Vehicle Engineering, Chongqing University of Technology, Chongqing 400054, China)

Abstract

An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. The analysis results show that the root mean square error (RMSE) of the variable temperature model is reduced by 90.187% compared with the non-variable temperature model. In addition, the SOC estimation error of the proposed NARX-AEKF fusion estimation method based on the variable temperature model remains within 2.055%. Even when the SOC initial error is 0.150, the NARX-AEKF fusion estimation method can quickly converge to the reference value within 5.000 s.

Suggested Citation

  • Chun Wang & Chaocheng Fang & Aihua Tang & Bo Huang & Zhigang Zhang, 2022. "A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty," Energies, MDPI, vol. 15(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4309-:d:837295
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    References listed on IDEAS

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    1. Chen, Xiaokai & Lei, Hao & Xiong, Rui & Shen, Weixiang & Yang, Ruixin, 2019. "A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    2. Wang, Ju & Xiong, Rui & Li, Linlin & Fang, Yu, 2018. "A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach," Applied Energy, Elsevier, vol. 229(C), pages 648-659.
    3. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature," Energies, MDPI, vol. 11(9), pages 1-17, September.
    4. Xu Lei & Xi Zhao & Guiping Wang & Weiyu Liu, 2019. "A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power," Energies, MDPI, vol. 12(19), pages 1-24, September.
    5. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
    6. Xi, Zhimin & Wang, Rui & Fu, Yuhong & Mi, Chris, 2022. "Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons," Applied Energy, Elsevier, vol. 305(C).
    7. Cheng Siong Chin & Zuchang Gao, 2018. "State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine," Energies, MDPI, vol. 11(4), pages 1-30, March.
    8. Tian, Jinpeng & Xiong, Rui & Shen, Weixiang & Lu, Jiahuan, 2021. "State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach," Applied Energy, Elsevier, vol. 291(C).
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