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Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications

Author

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  • Jufeng Yang

    (Department of Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA)

  • Bing Xia

    (Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
    Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA)

  • Yunlong Shang

    (Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
    School of Control Science and Engineering, Shandong University, Jinan 250061, China)

  • Wenxin Huang

    (Department of Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Chris Mi

    (Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA)

Abstract

This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.

Suggested Citation

  • Jufeng Yang & Bing Xia & Yunlong Shang & Wenxin Huang & Chris Mi, 2016. "Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications," Energies, MDPI, vol. 10(1), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:10:y:2016:i:1:p:5-:d:85912
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    References listed on IDEAS

    as
    1. Zhongyue Zou & Jun Xu & Chris Mi & Binggang Cao & Zheng Chen, 2014. "Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries," Energies, MDPI, vol. 7(8), pages 1-18, August.
    2. Xia, Bing & Zhao, Xin & de Callafon, Raymond & Garnier, Hugues & Nguyen, Truong & Mi, Chris, 2016. "Accurate Lithium-ion battery parameter estimation with continuous-time system identification methods," Applied Energy, Elsevier, vol. 179(C), pages 426-436.
    3. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    4. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    5. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    6. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
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    Cited by:

    1. Andrea Carloni & Federico Baronti & Roberto Di Rienzo & Roberto Roncella & Roberto Saletti, 2021. "An Open-Hardware and Low-Cost Maintenance Tool for Light-Electric-Vehicle Batteries," Energies, MDPI, vol. 14(16), pages 1-10, August.
    2. Yang, Jufeng & Huang, Wenxin & Xia, Bing & Mi, Chris, 2019. "The improved open-circuit voltage characterization test using active polarization voltage reduction method," Applied Energy, Elsevier, vol. 237(C), pages 682-694.

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