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A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles

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  • Xiong, Rui
  • Sun, Fengchun
  • Gong, Xianzhi
  • Gao, Chenchen

Abstract

An accurate State of Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. The paper attempts to make three contributions. (1) Through the recursive least square algorithm based identification method, the parameter of the lumped parameter battery model can be updated at each sampling interval with the real-time measurement of battery current and voltage, which is called the data-driven method. Note that the battery model has been improved with a simple electrochemical equation for describing the open circuit voltage against different aging levels and SoC. (2) Through the real-time updating technique of model parameter, a data-driven based adaptive SoC estimator is established with an adaptive extended Kalman filter. It has the potential to overcome the estimation error against battery degradation and varied operating environments. (3) The approach has been verified by different loading profiles of various health states of Lithium-ion polymer battery (LiPB) cells. The results indicate that the maximum estimation errors of voltage and SoC are less than 1% and 1.5% respectively.

Suggested Citation

  • Xiong, Rui & Sun, Fengchun & Gong, Xianzhi & Gao, Chenchen, 2014. "A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 1421-1433.
    • Handle: RePEc:eee:appene:v:113:y:2014:i:c:p:1421-1433
    DOI: 10.1016/j.apenergy.2013.09.006
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    References listed on IDEAS

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    1. Dai, Haifeng & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan & Gu, Weijun, 2012. "Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications," Applied Energy, Elsevier, vol. 95(C), pages 227-237.
    2. 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.
    3. Caiping Zhang & Jiuchun Jiang & Weige Zhang & Suleiman M. Sharkh, 2012. "Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering," Energies, MDPI, vol. 5(4), pages 1-18, April.
    4. Yinjiao Xing & Eden W. M. Ma & Kwok L. Tsui & Michael Pecht, 2011. "Battery Management Systems in Electric and Hybrid Vehicles," Energies, MDPI, vol. 4(11), pages 1-18, October.
    5. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
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