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A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique

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  • Sun, Fengchun
  • Xiong, Rui
  • He, Hongwen

Abstract

In order to maximize the capacity/energy utilization and guarantee safe and reliable operation of battery packs used in electric vehicles, an accurate cell state-of-charge (SoC) estimator is an essential part. This paper tries to add three contributions to the existing literature. (1) An integrated battery system identification method for model order determination and parameter identification is proposed. In addition to being able to identify the model parameters, it can also locate an optimal balance between model complexity and prediction precision. (2) A radial basis function (RBF) neural network based uncertainty quantification algorithm has been proposed for constructing response surface approximate model (RSAM) of model bias function. Based on the RSAM, the average pack model can be applied to every single cell in battery pack and realize accurate terminal voltage prediction. (3) A systematic SoC estimation framework for multi-cell series-connected battery pack of electric vehicles using bias correction technique has been proposed. Finally, three cases with twelve lithium-ion polymer battery (LiPB) cells series-connected battery pack are used to verify and evaluate the proposed framework. The result indicates that with the proposed systematic estimation framework the maximum absolute SoC estimation error of all cells in the battery pack are less than 2%.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:162:y:2016:i:c:p:1399-1409
    DOI: 10.1016/j.apenergy.2014.12.021
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Nagy, Tibor & Turányi, Tamás, 2012. "Determination of the uncertainty domain of the Arrhenius parameters needed for the investigation of combustion kinetic models," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 29-34.
    4. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    5. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
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