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A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method

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  • Zheng, Yuejiu
  • Cui, Yifan
  • Han, Xuebing
  • Ouyang, Minggao

Abstract

The empirical model is an open-loop model for battery capacity prediction, which faces the general problem of parameter mismatch. The data-driven method can realize the closed-loop estimation of capacity, but the data quality and model adaptability may lead to large noise. This paper proposes a framework of battery capacity prediction based on the feedforward empirical model and the feedback data-driven method. With the difference of the estimated capacities from the two models, parameters of the empirical model are modified, and the fusion capacity is finally predicted. Two cases based on different empirical models and fusion methods are introduced, one is based on discrete Arrhenius aging model with sequent extended Kalman filters (Case Ⅰ)，and the other is based on the linear aging model with incremental PID + Luenberger observer (Case Ⅱ). The two cases are verified by the aging experimental data. The results show that under the proposed capacity prediction framework, the model parameters can be effectively corrected and gradually converged, and the accurate capacity prediction can be achieved with battery aging. With the low calculation load, the predicted fusion capacity can still guarantee high accuracy in Case Ⅱ, which is a good choice for the online capacity prediction.

Suggested Citation

  • Zheng, Yuejiu & Cui, Yifan & Han, Xuebing & Ouyang, Minggao, 2021. "A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method," Energy, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:energy:v:237:y:2021:i:c:s0360544221018041
    DOI: 10.1016/j.energy.2021.121556
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    References listed on IDEAS

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    3. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
    4. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).
    5. Liyuan Shao & Yong Zhang & Xiujuan Zheng & Xin He & Yufeng Zheng & Zhiwei Liu, 2023. "A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods," Energies, MDPI, vol. 16(3), pages 1-22, February.
    6. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    7. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
    8. Sun, Tao & Xu, Yuwen & Feng, Lihong & Xu, Bowen & Chen, Dizuo & Zhang, Fang & Han, Xuebing & Zhao, Lihui & Zheng, Yuejiu, 2022. "A vehicle-cloud collaboration strategy for remaining driving range estimation based on online traffic route information and future operation condition prediction," Energy, Elsevier, vol. 248(C).
    9. Park, Sung-Won & Son, Sung-Yong, 2023. "Techno-economic analysis for the electric vehicle battery aging management of charge point operator," Energy, Elsevier, vol. 280(C).
    10. Kang, Jihyeon & Atwair, Mohamed & Nam, Inho & Lee, Chul-Jin, 2023. "Experimental and numerical investigation on effects of thickness of NCM622 cathode in Li-ion batteries for high energy and power density," Energy, Elsevier, vol. 263(PE).

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