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Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

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  • Xinyue Liu

    (Ningxia Engineering Research Center for Hybrid Manufacturing System, 204th Wenchang North Street, Xixia District, Yinchuan 750021, China
    School of Electrical and Information Engineering, North Minzu University, 204th Wenchang North Street, Xixia District, Yinchuan 750021, China)

  • Yang Gao

    (Ningxia Engineering Research Center for Hybrid Manufacturing System, 204th Wenchang North Street, Xixia District, Yinchuan 750021, China
    College of Mechatronic Engineering, North Minzu University, 204th Wenchang North Street, Xixia District, Yinchuan 750021, China)

  • Kyamra Marma

    (Department of Mechanical Engineering, University of Kansas, 3138 Learned Hall, 1530 W. 15th Street, Lawrence, KS 66045-4709, USA)

  • Yu Miao

    (Ningxia Engineering Research Center for Hybrid Manufacturing System, 204th Wenchang North Street, Xixia District, Yinchuan 750021, China
    School of Electrical and Information Engineering, North Minzu University, 204th Wenchang North Street, Xixia District, Yinchuan 750021, China)

  • Lin Liu

    (Department of Mechanical Engineering, University of Kansas, 3138 Learned Hall, 1530 W. 15th Street, Lawrence, KS 66045-4709, USA)

Abstract

This study explores the challenges and advances in the estimation of the state of charge (SOC) of lithium-ion batteries (LIBs), which are crucial to optimizing their performance and lifespan. This review focuses on four main techniques of SOC estimation: experimental measurement, modeling approach, data-driven approach, and joint estimation approach, highlighting the limitations and potential inaccuracies of each method. This study suggests a combined approach, incorporating correction parameters and closed-loop feedback, to improve measurement accuracy. It introduces a multi-physics model that considers temperature, charging rate, and aging effects and proposes the integration of models and algorithms for optimal estimation of SOC. This research emphasizes the importance of considering temperature and aging factors in data-driven approaches. It suggests that the fusion of different methods could lead to more accurate SOC predictions, an important area for future research.

Suggested Citation

  • Xinyue Liu & Yang Gao & Kyamra Marma & Yu Miao & Lin Liu, 2024. "Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge," Energies, MDPI, vol. 17(7), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1643-:d:1366541
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    References listed on IDEAS

    as
    1. Du, Jiuyu & Liu, Ye & Mo, Xinying & Li, Yalun & Li, Jianqiu & Wu, Xiaogang & Ouyang, Minggao, 2019. "Impact of high-power charging on the durability and safety of lithium batteries used in long-range battery electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    2. 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).
    3. Ze Cheng & Jikao Lv & Yanli Liu & Zhihao Yan, 2014. "Estimation of State of Charge for Lithium-Ion Battery Based on Finite Difference Extended Kalman Filter," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-10, April.
    4. Ali Wadi & Mamoun Abdel-Hafez & Ala A. Hussein, 2022. "Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter," Energies, MDPI, vol. 15(10), pages 1-15, May.
    5. 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).
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    Cited by:

    1. Yu Miao & Yang Gao & Xinyue Liu & Yuan Liang & Lin Liu, 2025. "Analysis of State-of-Charge Estimation Methods for Li-Ion Batteries Considering Wide Temperature Range," Energies, MDPI, vol. 18(5), pages 1-27, February.

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