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A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis

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  • Zhong, Liang
  • Zhang, Chenbin
  • He, Yao
  • Chen, Zonghai

Abstract

The state-of-charge (SOC) is a critical parameter of a Li-ion battery pack. Differences among in-pack cells are inevitable and can change the total capacity of a pack and the remaining available capacity. Because the traditional methods for the estimation of the SOC of a pack did not consider the difference among the cells and the impact of balance control, we developed a new method that accounts for these problems. To accurately estimate the pack SOC, we establish the relationship between the parameters of the pack and those of in-pack cells under different balance control strategies. This paper also studies the two different types of connections of a battery pack: in series and in parallel. Based on the model of the first over-charged cell and that of the first over-discharged cell, the estimation of the SOC of a battery pack is realized by the Unscented Particle Filter (UPF) algorithm. A simulation experiment verified the method for the estimation of the SOC for a battery pack based on actual data and proved that an accurate estimation value can be obtained by the method.

Suggested Citation

  • Zhong, Liang & Zhang, Chenbin & He, Yao & Chen, Zonghai, 2014. "A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis," Applied Energy, Elsevier, vol. 113(C), pages 558-564.
    • Handle: RePEc:eee:appene:v:113:y:2014:i:c:p:558-564
    DOI: 10.1016/j.apenergy.2013.08.008
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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. He, Yao & Liu, XingTao & Zhang, ChenBin & Chen, ZongHai, 2013. "A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries," Applied Energy, Elsevier, vol. 101(C), pages 808-814.
    3. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    4. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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