IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i4p2313-d752108.html
   My bibliography  Save this article

Early Prognostics of Lithium-Ion Battery Pack Health

Author

Listed:
  • Jiwei Wang

    (Graduate School of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan)

  • Zhongwei Deng

    (College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China)

  • Kaile Peng

    (College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China)

  • Xinchen Deng

    (College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China)

  • Lijun Xu

    (Xinjiang Coal Mine Electromechanical Engineering Technology Research Center, Xinjiang Institute of Engineering, Urumqi 830023, China)

  • Guoqing Guan

    (Graduate School of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan)

  • Abuliti Abudula

    (Graduate School of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan)

Abstract

Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.

Suggested Citation

  • Jiwei Wang & Zhongwei Deng & Kaile Peng & Xinchen Deng & Lijun Xu & Guoqing Guan & Abuliti Abudula, 2022. "Early Prognostics of Lithium-Ion Battery Pack Health," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2313-:d:752108
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/4/2313/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/4/2313/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Galeotti, Matteo & Cinà, Lucio & Giammanco, Corrado & Cordiner, Stefano & Di Carlo, Aldo, 2015. "Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy," Energy, Elsevier, vol. 89(C), pages 678-686.
    2. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    3. Weng, Caihao & Feng, Xuning & Sun, Jing & Peng, Huei, 2016. "State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking," Applied Energy, Elsevier, vol. 180(C), pages 360-368.
    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. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    2. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    3. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    4. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    5. Zhang, Cetengfei & Zhou, Quan & Hua, Min & Xu, Hongming & Bassett, Mike & Zhang, Fanggang, 2023. "Cuboid equivalent consumption minimization strategy for energy management of multi-mode plug-in hybrid vehicles considering diverse time scale objectives," Applied Energy, Elsevier, vol. 351(C).
    6. Lin, Yan-Hui & Ruan, Sheng-Jia & Chen, Yun-Xia & Li, Yan-Fu, 2023. "Physics-informed deep learning for lithium-ion battery diagnostics using electrochemical impedance spectroscopy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    7. Vichard, L. & Ravey, A. & Venet, P. & Harel, F. & Pelissier, S. & Hissel, D., 2021. "A method to estimate battery SOH indicators based on vehicle operating data only," Energy, Elsevier, vol. 225(C).
    8. Chi Zhang & Fuwu Yan & Changqing Du & Jianqiang Kang & Richard Fiifi Turkson, 2017. "Evaluating the Degradation Mechanism and State of Health of LiFePO 4 Lithium-Ion Batteries in Real-World Plug-in Hybrid Electric Vehicles Application for Different Ageing Paths," Energies, MDPI, vol. 10(1), pages 1-13, January.
    9. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    10. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    11. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    12. Kaizhi Liang & Zhaosheng Zhang & Peng Liu & Zhenpo Wang & Shangfeng Jiang, 2019. "Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-17, December.
    13. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
    14. Shida Jiang & Zhengxiang Song, 2021. "Estimating the State of Health of Lithium-Ion Batteries with a High Discharge Rate through Impedance," Energies, MDPI, vol. 14(16), pages 1-20, August.
    15. Lai, Xin & Yi, Wei & Cui, Yifan & Qin, Chao & Han, Xuebing & Sun, Tao & Zhou, Long & Zheng, Yuejiu, 2021. "Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter," Energy, Elsevier, vol. 216(C).
    16. Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2017. "Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes," Energy, Elsevier, vol. 135(C), pages 257-268.
    17. Mohammed, Abubakar Gambo & Elfeky, Karem Elsayed & Wang, Qiuwang, 2022. "Recent advancement and enhanced battery performance using phase change materials based hybrid battery thermal management for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    18. Farmann, Alexander & Waag, Wladislaw & Sauer, Dirk Uwe, 2016. "Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles," Energy, Elsevier, vol. 112(C), pages 294-306.
    19. Chang, Chun & Wu, Yutong & Jiang, Jiuchun & Jiang, Yan & Tian, Aina & Li, Taiyu & Gao, Yang, 2022. "Prognostics of the state of health for lithium-ion battery packs in energy storage applications," Energy, Elsevier, vol. 239(PB).
    20. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).

    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:jsusta:v:14:y:2022:i:4:p:2313-:d:752108. 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.