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State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy

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  • Li, Xiaoyu
  • Xu, Jianhua
  • Hong, Jianxun
  • Tian, Jindong
  • Tian, Yong

Abstract

Due to the inconsistency among battery cells, it is very difficult to estimate the state of energy (SOE) of a battery pack online. In this paper, an adaptive SOE estimation method for a series-connected lithium-ion battery pack based on representative cells is proposed. The dynamic characteristics of a battery are modeled by a first-order resistor-capacitor model. The key parameters and the SOEs of the representative cells are estimated by the recursive least squares algorithm and an adaptive cubature Kalman filter, respectively. The SOE of the series-connected battery pack is obtained by weighting the SOEs of the representative cells based on an adaptive strategy. Experimental results indicate that the SOE estimation result of the series-connected battery pack is close to the SOE of the “strongest” representative cell at the fully charged state, while it is close to the SOE of the “weakest” representative cell at the ending point of discharging. Even with a large initial error, the estimated SOE can quickly track the reference value. The root-mean square errors of the SOE estimation results at 25 °C, 50 °C and 0 °C are 1.3%, 2.2% and 1.7%, respectively.

Suggested Citation

  • Li, Xiaoyu & Xu, Jianhua & Hong, Jianxun & Tian, Jindong & Tian, Yong, 2021. "State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy," Energy, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220319654
    DOI: 10.1016/j.energy.2020.118858
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    References listed on IDEAS

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