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Joint estimation of lithium-ion battery state of charge and capacity within an adaptive variable multi-timescale framework considering current measurement offset

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  • Jiang, Bo
  • Dai, Haifeng
  • Wei, Xuezhe
  • Xu, Tianjiao

Abstract

Accurate and reliable estimation of battery state of charge (SOC) and capacity is essential for the management of the lithium-ion battery in electric vehicles. In this paper, a novel joint estimation approach of battery SOC and capacity with an adaptive variable multi-timescale framework is proposed, which also deals with the interference of current measurement offset (CMO) effectively. Aiming at the problem of unknown CMO, which will affect the accuracy of battery modeling and state estimation, an original two-stage recursive least squares algorithm is raised to identify the battery model parameters and the CMO quickly. The adaptive extended Kalman filter is applied to improve the SOC estimation accuracy by updating the noise covariance adaptively, and the recursive total least squares is used to estimate capacity with the consideration that both the battery SOC estimation and charge accumulation suffer from noises. Finally, a joint estimation of SOC and capacity structure is founded, and to address the issue of different varying characteristics of battery SOC and capacity, a novel adaptive variable multi-timescale framework is proposed. The experimental results indicate the accuracy, convergence, and adaptivity of the proposed method in different working conditions.

Suggested Citation

  • Jiang, Bo & Dai, Haifeng & Wei, Xuezhe & Xu, Tianjiao, 2019. "Joint estimation of lithium-ion battery state of charge and capacity within an adaptive variable multi-timescale framework considering current measurement offset," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:81
    DOI: 10.1016/j.apenergy.2019.113619
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    14. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    15. Jiang, Yan & Meng, Xin, 2023. "A battery capacity estimation method based on the equivalent circuit model and quantile regression using vehicle real-world operation data," Energy, Elsevier, vol. 284(C).
    16. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    17. 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).
    18. Jiang, Yihui & Xu, Jun & Hou, Wenlong & Mei, Xuesong, 2021. "A stack pressure based equivalent mechanical model of lithium-ion pouch batteries," Energy, Elsevier, vol. 221(C).
    19. 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).
    20. Lu, Zhenfeng & Fei, Zicheng & Wang, Benfei & Yang, Fangfang, 2024. "A feature fusion-based convolutional neural network for battery state-of-health estimation with mining of partial voltage curve," Energy, Elsevier, vol. 288(C).
    21. Zhang, Shuzhi & Zhang, Chen & Jiang, Shiyong & Zhang, Xiongwen, 2022. "A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation," Energy, Elsevier, vol. 246(C).
    22. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    23. Longxing Wu & Kai Liu & Hui Pang & Jiamin Jin, 2021. "Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias," Energies, MDPI, vol. 14(17), pages 1-12, August.
    24. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    25. Jiang, Yihui & Xu, Jun & Liu, Mengmeng & Mei, Xuesong, 2022. "An electromechanical coupling model-based state of charge estimation method for lithium-ion pouch battery modules," Energy, Elsevier, vol. 259(C).

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