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Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects

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  • Lai, Xin
  • Huang, Yunfeng
  • Gu, Huanghui
  • Han, Xuebing
  • Feng, Xuning
  • Dai, Haifeng
  • Zheng, Yuejiu
  • Ouyang, Minggao

Abstract

The estimation of remaining discharge energy (RDE) of lithium-ion batteries is the basis for the remaining driving range estimation of electric vehicles. The RDE estimation is affected by many factors, such as battery future load, battery ageing and temperature. In this study, an RDE estimation method based on the future load prediction considering battery temperature and ageing effects is proposed. First, the hidden Markov model (HMM) is implemented to predict the future load of battery. Then, the capacity test at different temperatures is conducted to determine the limited state-of-charge (SOC) in the prediction field. Third, a forgetting factor recursive least square (FFRLS) algorithm is used to identify and update the battery model parameters online to address the parameter mismatch issue caused by battery ageing and temperature fluctuation. Finally, based on the predicted current, SOC, and voltage sequences, the RDE is estimated under different operation conditions. In particular, a battery simulation driving condition is constructed using the real vehicle speed to verify the effectiveness of the proposed method in complex conditions. The results demonstrate that the accuracy and robustness of the proposed method against various operation conditions and battery ageing are satisfactory.

Suggested Citation

  • Lai, Xin & Huang, Yunfeng & Gu, Huanghui & Han, Xuebing & Feng, Xuning & Dai, Haifeng & Zheng, Yuejiu & Ouyang, Minggao, 2022. "Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects," Energy, Elsevier, vol. 238(PA).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221020028
    DOI: 10.1016/j.energy.2021.121754
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    7. Sun, Tao & Wang, Shaoqing & Jiang, Sheng & Xu, Bowen & Han, Xuebing & Lai, Xin & Zheng, Yuejiu, 2022. "A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning," Energy, Elsevier, vol. 239(PC).
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