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A novel state of charge estimation method for lithium-ion batteries based on bias compensation

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  • Ouyang, Tiancheng
  • Xu, Peihang
  • Chen, Jingxian
  • Su, Zixiang
  • Huang, Guicong
  • Chen, Nan

Abstract

Accurate and efficient state-of-charge estimation for lithium-ion batteries are extremely crucial for electrical vehicles. A lot of researches make great progress on joint estimation algorithms. However, the combination of different algorithms brings too many design parameters, which reduces the accuracy of estimation and computational efficiency. In this paper, an adaptive H-infinity filter with bias compensation is proposed. Static condition and dynamic condition are set to verify the proposed algorithm in the experiments. In the dynamic condition, the proposed algorithm is verified and compared with the other three joint estimation algorithms at temperatures of 40 °C, 25 °C, 10 °C and 0 °C. Experiments show that the proposed algorithm achieves the highest estimation accuracy and calculation efficiency under two operating conditions and four temperatures, and the average time consumption of the proposed algorithm is reduced by 0.9%, 2.25% and 34.14%, respectively, compared with the other combinations of different algorithms.

Suggested Citation

  • Ouyang, Tiancheng & Xu, Peihang & Chen, Jingxian & Su, Zixiang & Huang, Guicong & Chen, Nan, 2021. "A novel state of charge estimation method for lithium-ion batteries based on bias compensation," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221005971
    DOI: 10.1016/j.energy.2021.120348
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    Cited by:

    1. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    2. 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).
    3. Jiang, Bo & Tao, Siyi & Wang, Xueyuan & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique," Energy, Elsevier, vol. 278(PA).
    4. John H. T. Luong & Cang Tran & Di Ton-That, 2022. "A Paradox over Electric Vehicles, Mining of Lithium for Car Batteries," Energies, MDPI, vol. 15(21), pages 1-25, October.
    5. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    6. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).

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