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Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model

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  • Xu, Zhicheng
  • Wang, Jun
  • Lund, Peter D.
  • Zhang, Yaoming

Abstract

Accurate estimation of the state of charge (SOC) and state of health (SOH) is a fundamental requirement for the management system of a lithium-ion battery, but also important to the safety and operational effectiveness of electric vehicles and energy storage systems. Here a model-based method is implemented to assess the SOC and SOH simultaneously. An equivalent circuit model is employed to describe the battery dynamics with recursive least squares online identifying model parameters and unscented Kalman filter estimating battery state. A minimalist electrochemical model is proposed to describe the distribution of the lithium content inside the battery relating the SOH to the capacity fading due to irreversible loss of Li. Based on the real-time capacity value, the state of charge could further be estimated. Comparing the experimental results shows that the battery capacity, i.e., SOH could be predicted timely with a mean error around 2%, which confirms the validity of the proposed co-estimation method for SOC and SOH.

Suggested Citation

  • Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:energy:v:240:y:2022:i:c:s0360544221030644
    DOI: 10.1016/j.energy.2021.122815
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    Citations

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    Cited by:

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    4. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    5. Tang, Aihua & Wu, Xinyu & Xu, Tingting & Hu, Yuanzhi & Long, Shengwen & Yu, Quanqing, 2024. "State of health estimation based on inconsistent evolution for lithium-ion battery module," Energy, Elsevier, vol. 286(C).
    6. Ye, Jinhua & Xie, Quan & Lin, Mingqiang & Wu, Ji, 2024. "A method for estimating the state of health of lithium-ion batteries based on physics-informed neural network," Energy, Elsevier, vol. 294(C).
    7. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    8. Xiang Bao & Yuefeng Liu & Bo Liu & Haofeng Liu & Yue Wang, 2023. "Multi-State Online Estimation of Lithium-Ion Batteries Based on Multi-Task Learning," Energies, MDPI, vol. 16(7), pages 1-20, March.
    9. Zhang, Chaolong & Luo, Laijin & Yang, Zhong & Du, Bolun & Zhou, Ziheng & Wu, Ji & Chen, Liping, 2024. "Flexible method for estimating the state of health of lithium-ion batteries using partial charging segments," Energy, Elsevier, vol. 295(C).
    10. Mehrshad Pakjoo & Luigi Piegari & Giuliano Rancilio & Silvia Colnago & Joseph Epoupa Mengou & Federico Bresciani & Giacomo Gorni & Stefano Mandelli & Marco Merlo, 2023. "A Review on Testing of Electrochemical Cells for Aging Models in BESS," Energies, MDPI, vol. 16(19), pages 1-26, September.
    11. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).
    12. Duan, Linchao & Zhang, Xugang & Jiang, Zhigang & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2023. "State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis," Energy, Elsevier, vol. 280(C).
    13. Qiao, Jialu & Wang, Shunli & Yu, Chunmei & Yang, Xiao & Fernandez, Carlos, 2023. "A chaotic firefly - Particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance," Energy, Elsevier, vol. 263(PE).
    14. Braco, Elisa & San Martín, Idoia & Sanchis, Pablo & Ursúa, Alfredo & Stroe, Daniel-Ioan, 2022. "State of health estimation of second-life lithium-ion batteries under real profile operation," Applied Energy, Elsevier, vol. 326(C).
    15. Zhang, Ran & Ji, ChunHui & Zhou, Xing & Liu, Tianyu & Jin, Guang & Pan, Zhengqiang & Liu, Yajie, 2024. "Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression," Energy, Elsevier, vol. 297(C).
    16. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    17. Hou, Jiayang & Xu, Jun & Lin, Chuanping & Jiang, Delong & Mei, Xuesong, 2024. "State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method," Energy, Elsevier, vol. 290(C).
    18. Li, Xining & Ju, Lingling & Geng, Guangchao & Jiang, Quanyuan, 2023. "Data-driven state-of-health estimation for lithium-ion battery based on aging features," Energy, Elsevier, vol. 274(C).
    19. Park, Shina & Song, Youngbin & Kim, Sang Woo, 2024. "Simultaneous diagnosis of cell aging and internal short circuit faults in lithium-ion batteries using average leakage interval," Energy, Elsevier, vol. 290(C).
    20. 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).

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