IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i7p1643-d1366541.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/7/1643/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/7/1643/
    Download Restriction: no
    ---><---

    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Jingwen & Chen, Chunlin, 2021. "A multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries," Energy, Elsevier, vol. 229(C).
    2. Wang, Jiajia & Yue, Xiyan & Wang, Peifen & Yu, Tao & Du, Xiao & Hao, Xiaogang & Abudula, Abuliti & Guan, Guoqing, 2022. "Electrochemical technologies for lithium recovery from liquid resources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    3. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
    4. Li, Xiaoyu & Xu, Jianhua & Hong, Jianxun & Tian, Jindong & Tian, Yong, 2021. "State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy," Energy, Elsevier, vol. 214(C).
    5. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    6. Qin, Yudi & Du, Jiuyu & Lu, Languang & Gao, Ming & Haase, Frank & Li, Jianqiu & Ouyang, Minggao, 2020. "A rapid lithium-ion battery heating method based on bidirectional pulsed current: Heating effect and impact on battery life," Applied Energy, Elsevier, vol. 280(C).
    7. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    8. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    9. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    10. Ashikur Rahman & Xianke Lin & Chongming Wang, 2022. "Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer," Energies, MDPI, vol. 15(15), pages 1-19, August.
    11. Panagiotis Eleftheriadis & Spyridon Giazitzis & Sonia Leva & Emanuele Ogliari, 2023. "Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview," Forecasting, MDPI, vol. 5(3), pages 1-24, September.
    12. 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).
    13. Han, Gaoce & Yan, Jize & Guo, Zhen & Greenwood, David & Marco, James & Yu, Yifei, 2021. "A review on various optical fibre sensing methods for batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    14. Arkadiusz Małek & Jacek Caban & Agnieszka Dudziak & Andrzej Marciniak & Piotr Ignaciuk, 2023. "A Method of Assessing the Selection of Carport Power for an Electric Vehicle Using the Metalog Probability Distribution Family," Energies, MDPI, vol. 16(13), pages 1-16, June.
    15. Zhengxin, Jiang & Qin, Shi & Yujiang, Wei & Hanlin, Wei & Bingzhao, Gao & Lin, He, 2021. "An Immune Genetic Extended Kalman Particle Filter approach on state of charge estimation for lithium-ion battery," Energy, Elsevier, vol. 230(C).
    16. Zhang, Guangxu & Wei, Xuezhe & Tang, Xuan & Zhu, Jiangong & Chen, Siqi & Dai, Haifeng, 2021. "Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    17. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    18. Costa, Alexis & Coppitters, Diederik & Dubois, Lionel & Contino, Francesco & Thomas, Diane & De Weireld, Guy, 2024. "Energy, exergy, economic and environmental (4E) analysis of a cryogenic carbon purification unit with membrane for oxyfuel cement plant flue gas," Applied Energy, Elsevier, vol. 357(C).
    19. Rajib Mahamud & Chanwoo Park, 2022. "Theory and Practices of Li-Ion Battery Thermal Management for Electric and Hybrid Electric Vehicles," Energies, MDPI, vol. 15(11), pages 1-45, May.
    20. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1643-:d:1366541. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.