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Load-responsive model switching estimation for state of charge of lithium-ion batteries

Author

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  • Tang, Xiaopeng
  • Gao, Furong
  • Zou, Changfu
  • Yao, Ke
  • Hu, Wengui
  • Wik, Torsten

Abstract

Accurately estimating state of charge (SoC) is very important to enable advanced management of lithium-ion batteries, however technical challenges mainly exist in the lack of a high-fidelity battery model whose parameters are sensitive to changes of the state and load condition. To address the problem, this paper explores and proposes a model switching estimation algorithm that online selects the most suitable model from its model library based on the relationship between load conditions for calibration and in practice. By leveraging a high-pass filter and the Coulomb counting, an event trigger procedure is developed to detect the estimation performance and then determine timely switching actions. This estimation algorithm is realized by adopting a gradient correction method for system identification and the unscented Kalman filter and H∞ observer for state estimation. Experimental results illustrate that the proposed algorithm is able to reproduce SoC trajectories under various operating profiles, with the root-mean-square errors bounded by 2.22%. The efficacy of this algorithm is further corroborated by comparing to single model-based estimators and two prevalent adaptive SoC estimators.

Suggested Citation

  • Tang, Xiaopeng & Gao, Furong & Zou, Changfu & Yao, Ke & Hu, Wengui & Wik, Torsten, 2019. "Load-responsive model switching estimation for state of charge of lithium-ion batteries," Applied Energy, Elsevier, vol. 238(C), pages 423-434.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:423-434
    DOI: 10.1016/j.apenergy.2019.01.057
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    2. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    3. Wu, Lifeng & Zhang, Yu, 2023. "Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery," Energy, Elsevier, vol. 268(C).
    4. Wei, Zhongbao & Hu, Jian & Li, Yang & He, Hongwen & Li, Weihan & Sauer, Dirk Uwe, 2022. "Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries," Applied Energy, Elsevier, vol. 307(C).
    5. Fang Liu & Jie Ma & Weixing Su & Hanning Chen & Maowei He, 2020. "Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation," Energies, MDPI, vol. 13(7), pages 1-19, April.
    6. Zhu, Rui & Duan, Bin & Zhang, Junming & Zhang, Qi & Zhang, Chenghui, 2020. "Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter," Applied Energy, Elsevier, vol. 277(C).
    7. Ran Li & Hui Sun & Xue Wei & Weiwen Ta & Haiying Wang, 2022. "Lithium Battery State-of-Charge Estimation Based on AdaBoost.Rt-RNN," Energies, MDPI, vol. 15(16), pages 1-15, August.
    8. Lai, Xin & Yao, Yi & Tang, Xiaopeng & Zheng, Yuejiu & Zhou, Yuanqiang & Sun, Yuedong & Gao, Furong, 2023. "Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions," Energy, Elsevier, vol. 282(C).
    9. Zhang, Chaoyu & Zhang, Chengming & Li, Liyi & Guo, Qingbo, 2021. "Parameter analysis of power system for solar-powered unmanned aerial vehicle," Applied Energy, Elsevier, vol. 295(C).
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    11. Lee, Won Yeol & Jin, En Mei & Cho, Jung Sang & Kang, Dong-Won & Jin, Bo & Jeong, Sang Mun, 2020. "Freestanding flexible multilayered Sulfur–Carbon nanotubes for Lithium–Sulfur battery cathodes," Energy, Elsevier, vol. 212(C).
    12. Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    13. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).

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