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A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model

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  • Zheng, Yuejiu
  • Qin, Chao
  • Lai, Xin
  • Han, Xuebing
  • Xie, Yi

Abstract

Practical open-loop capacity estimation models, such as the Arrhenius aging model, require parameter updating for the real-world capacity estimation in order to guarantee the estimation accuracy. In this paper, a novel capacity estimation method for lithium-ion batteries, based on the fusion estimation of charging curve sections and the discrete Arrhenius aging model using sequential extended Kalman filters, is proposed. The estimation method based on fractional charging curves is developed to estimate the battery capacity during vehicle charging, and the estimation results serve as the feedback using the first Kalman filter to update the model parameters of the discrete Arrhenius aging model. Then, the second Kalman filter makes a fusion capacity estimation based on the results of charging curve sections and the discrete Arrhenius aging model with the modified parameters. The results of the cycle life tests show that the proposed algorithm can modify the parameters of the discrete Arrhenius aging model online. And the fusion capacity estimation error is less than 1% when the model parameters reach a steady state.

Suggested Citation

  • Zheng, Yuejiu & Qin, Chao & Lai, Xin & Han, Xuebing & Xie, Yi, 2019. "A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:27
    DOI: 10.1016/j.apenergy.2019.113327
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    11. 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).
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    14. Ma, Jian & Shang, Pengchao & Zou, Xinyu & Ma, Ning & Ding, Yu & Sun, Jinwen & Cheng, Yujie & Tao, Laifa & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2021. "A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries," Applied Energy, Elsevier, vol. 282(PA).
    15. Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
    16. Lai, Xin & Huang, Yunfeng & Deng, Cong & Gu, Huanghui & Han, Xuebing & Zheng, Yuejiu & Ouyang, Minggao, 2021. "Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    17. Hashemi, Seyed Reza & Mahajan, Ajay Mohan & Farhad, Siamak, 2021. "Online estimation of battery model parameters and state of health in electric and hybrid aircraft application," Energy, Elsevier, vol. 229(C).
    18. 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).
    19. 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).
    20. Lin, Yan-Hui & Ruan, Sheng-Jia & Chen, Yun-Xia & Li, Yan-Fu, 2023. "Physics-informed deep learning for lithium-ion battery diagnostics using electrochemical impedance spectroscopy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    21. Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).
    22. Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    23. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
    24. Chen, Jianguo & Han, Xuebing & Sun, Tao & Zheng, Yuejiu, 2024. "Analysis and prediction of battery aging modes based on transfer learning," Applied Energy, Elsevier, vol. 356(C).

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