IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v319y2025ics0360544225005213.html
   My bibliography  Save this article

Evaluation and prediction of lithium-ion battery pack inconsistency in electric vehicles based on actual operating data

Author

Listed:
  • Wang, Shichao
  • Wang, Yujie
  • Soo, Yin-Yi

Abstract

Battery inconsistency problems will inevitably occur in the process of battery operation after forming a pack, and the consistency of the battery pack is of great significance to the management, equalization and maintenance of the battery system. In this paper, a method for evaluating and predicting the inconsistency of lithium-ion battery pack in electric vehicles based on actual operating data is proposed. First, the recursive least squares with forgetting factor algorithm is used for the second-order equivalent circuit model parameter identification, and the ohmic internal resistance, electrochemical polarization internal resistance, and concentration difference polarization internal resistance are identified as the characteristic parameters from the electric vehicle operation data. Second, a comprehensive battery pack inconsistency evaluation model and a time series prediction model are proposed, which can accurately evaluate and predict the battery pack inconsistency. Finally, the algorithm is validated by nine months of actual electric vehicle operation dataset, and the results show that the proposed inconsistency evaluation and prediction method has high accuracy.

Suggested Citation

  • Wang, Shichao & Wang, Yujie & Soo, Yin-Yi, 2025. "Evaluation and prediction of lithium-ion battery pack inconsistency in electric vehicles based on actual operating data," Energy, Elsevier, vol. 319(C).
    • Handle: RePEc:eee:energy:v:319:y:2025:i:c:s0360544225005213
    DOI: 10.1016/j.energy.2025.134879
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225005213
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.134879?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Shuaishuai Lv & Xingxing Wang & Wenfan Lu & Jiaqiao Zhang & Hongjun Ni, 2021. "The Influence of Temperature on the Capacity of Lithium Ion Batteries with Different Anodes," Energies, MDPI, vol. 15(1), pages 1-15, December.
    3. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Xiangli, Kang & Meng, Dean, 2024. "A novel method for fault diagnosis and type identification of cell voltage inconsistency in electric vehicles using weighted Euclidean distance evaluation and statistical analysis," Energy, Elsevier, vol. 293(C).
    4. Fan, Xinyuan & Zhang, Weige & Sun, Bingxiang & Zhang, Junwei & He, Xitian, 2022. "Battery pack consistency modeling based on generative adversarial networks," Energy, Elsevier, vol. 239(PE).
    5. An, Fulai & Zhang, Weige & Sun, Bingxiang & Jiang, Jiuchun & Fan, Xinyuan, 2023. "A novel battery pack inconsistency model and influence degree analysis of inconsistency on output energy," 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. Tian, Jiaqiang & Fan, Yuan & Pan, Tianhong & Zhang, Xu & Yin, Jianning & Zhang, Qingping, 2024. "A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Xu, Maoshu & Zhang, E. & Wang, Sheng & Shen, Yi & Zou, Binchen & Li, Haomiao & Wan, Yiming & Wang, Kangli & Jiang, Kai, 2024. "Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures," Applied Energy, Elsevier, vol. 355(C).
    3. Fan, Xinyuan & Qi, Hongfeng & Zhang, Weige & Zhang, Yanru, 2024. "Experiment-free physical hybrid neural network approach for battery pack inconsistency estimation," Applied Energy, Elsevier, vol. 358(C).
    4. Zhang, Junwei & Zhang, Weige & Sun, Bingxiang & Zhang, Yanru & Fan, Xinyuan & Zhao, Bo, 2024. "A novel method of battery pack energy health estimation based on visual feature learning," Energy, Elsevier, vol. 293(C).
    5. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    6. 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).
    7. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    8. Zhang, Jianping & Zhang, Yinjie & Fu, Jian & Zhao, Dawen & Liu, Ping & Zhang, Zhiwei, 2024. "Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    9. Okay, Kamil & Eray, Sermet & Eray, Aynur, 2022. "Development of prototype battery management system for PV system," Renewable Energy, Elsevier, vol. 181(C), pages 1294-1304.
    10. Pan, Rui & Liu, Tongshen & Huang, Wei & Wang, Yuxin & Yang, Duo & Chen, Jie, 2023. "State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree," Energy, Elsevier, vol. 285(C).
    11. 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).
    12. Qi, Kaijian & Zhang, Weigang & Zhou, Wei & Cheng, Jifu, 2022. "Integrated battery power capability prediction and driving torque regulation for electric vehicles: A reduced order MPC approach," Applied Energy, Elsevier, vol. 317(C).
    13. Liu, Xinghua & Li, Siqi & Tian, Jiaqiang & Wei, Zhongbao & Wang, Peng, 2023. "Health estimation of lithium-ion batteries with voltage reconstruction and fusion model," Energy, Elsevier, vol. 282(C).
    14. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    15. Liu, Zheng & Zhao, Zhenhua & Qiu, Yuan & Jing, Benqin & Yang, Chunshan & Wu, Huifeng, 2023. "Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction," Energy, Elsevier, vol. 283(C).
    16. Gao, Tianhan & Lu, Wei, 2024. "Reduced-order electrochemical models with shape functions for fast, accurate prediction of lithium-ion batteries under high C-rates," Applied Energy, Elsevier, vol. 353(PA).
    17. Ma, Chen & Chang, Long & Cui, Naxin & Duan, Bin & Zhang, Yulong & Yu, Zhihao, 2022. "Statistical relationships between numerous retired lithium-ion cells and packs with random sampling for echelon utilization," Energy, Elsevier, vol. 257(C).
    18. Fabian Rücker & Ilka Schoeneberger & Till Wilmschen & Ahmed Chahbaz & Philipp Dechent & Felix Hildenbrand & Elias Barbers & Matthias Kuipers & Jan Figgener & Dirk Uwe Sauer, 2022. "A Comprehensive Electric Vehicle Model for Vehicle-to-Grid Strategy Development," Energies, MDPI, vol. 15(12), pages 1-31, June.
    19. Chu, Yunkun & Cui, Naxin & Liu, Kailong, 2025. "Nonlinear modeling and SOC estimation of lithium-ion batteries based on block-oriented structures," Energy, Elsevier, vol. 315(C).
    20. Adrian Ostermann & Yann Fabel & Kim Ouan & Hyein Koo, 2022. "Forecasting Charging Point Occupancy Using Supervised Learning Algorithms," Energies, MDPI, vol. 15(9), pages 1-23, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:energy:v:319:y:2025:i:c:s0360544225005213. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.