IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v283y2023ics0360544223018510.html
   My bibliography  Save this article

Aging mechanism analysis and capacity estimation of lithium - ion battery pack based on electric vehicle charging data

Author

Listed:
  • Sun, Tao
  • Chen, Jianguo
  • Wang, Shaoqing
  • Chen, Quanwei
  • Han, Xuebing
  • Zheng, Yuejiu

Abstract

Due to the incompleteness of charging data, the voltage step caused by fast charging conditions and sampling accuracy of the battery management system, the conventional mechanism model is not applicable to the aging mechanism analysis and capacity estimation of electric vehicle batteries. Therefore, this study applies support vector regression to achieve the actual charging condition equivalence based on the variable operating conditions charging data of electric vehicles. The aging parameters and open circuit voltage reconstruction based on the dual-tank model are applied to obtaining the aging state and the capacity of cells. The capacity of the battery pack is calculated by the pack formation theory. The maximum error of the aging parameters obtained by the multiple stage constant current is 5.572% compared with the 1/20 C (C is the charge/discharge current rate unit) constant current charging of the experimental battery. As to the maximum relative error of cell capacity estimation based on vehicle data is 0.99%, and battery pack capacity estimation is 0.86%. The method proposed in this paper is not only able to quantitatively analyze the dominant factors of battery capacity decay, but also achieves high accuracy capacity estimation of the vehicle battery pack and its individual cells.

Suggested Citation

  • Sun, Tao & Chen, Jianguo & Wang, Shaoqing & Chen, Quanwei & Han, Xuebing & Zheng, Yuejiu, 2023. "Aging mechanism analysis and capacity estimation of lithium - ion battery pack based on electric vehicle charging data," Energy, Elsevier, vol. 283(C).
  • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223018510
    DOI: 10.1016/j.energy.2023.128457
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223018510
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.128457?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Zheng, Linfeng & Zhang, Lei & Zhu, Jianguo & Wang, Guoxiu & Jiang, Jiuchun, 2016. "Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model," Applied Energy, Elsevier, vol. 180(C), pages 424-434.
    2. Sun, Tao & Wang, Shaoqing & Jiang, Sheng & Xu, Bowen & Han, Xuebing & Lai, Xin & Zheng, Yuejiu, 2022. "A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning," Energy, Elsevier, vol. 239(PC).
    3. 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.
    4. Ouyang, Minggao & Feng, Xuning & Han, Xuebing & Lu, Languang & Li, Zhe & He, Xiangming, 2016. "A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery," Applied Energy, Elsevier, vol. 165(C), pages 48-59.
    5. Zheng, Yuejiu & Wang, Jingjing & Qin, Chao & Lu, Languang & Han, Xuebing & Ouyang, Minggao, 2019. "A novel capacity estimation method based on charging curve sections for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 185(C), pages 361-371.
    6. 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.
    7. Zheng, Yuejiu & Qin, Chao & Lai, Xin & Han, Xuebing & Xie, Yi, 2019. "A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Tian, Jiaqiang & Xu, Ruilong & Wang, Yujie & Chen, Zonghai, 2021. "Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries," Energy, Elsevier, vol. 221(C).
    9. Lai, Xin & Huang, Yunfeng & Gu, Huanghui & Han, Xuebing & Feng, Xuning & Dai, Haifeng & Zheng, Yuejiu & Ouyang, Minggao, 2022. "Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects," Energy, Elsevier, vol. 238(PA).
    10. Xuning Feng & Caihao Weng & Xiangming He & Li Wang & Dongsheng Ren & Languang Lu & Xuebing Han & Minggao Ouyang, 2018. "Incremental Capacity Analysis on Commercial Lithium-Ion Batteries using Support Vector Regression: A Parametric Study," Energies, MDPI, vol. 11(9), pages 1-21, September.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fan, Chuanxin & Liu, Kailong & Zhu, Tao & Peng, Qiao, 2024. "Understanding of Lithium-ion battery degradation using multisine-based nonlinear characterization method," Energy, Elsevier, vol. 290(C).
    2. Chen, Jianguo & Han, Xuebing & Sun, Tao & Zheng, Yuejiu, 2024. "Analysis and prediction of battery aging modes based on transfer learning," Applied Energy, Elsevier, vol. 356(C).

    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. Sun, Tao & Wang, Shaoqing & Jiang, Sheng & Xu, Bowen & Han, Xuebing & Lai, Xin & Zheng, Yuejiu, 2022. "A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning," Energy, Elsevier, vol. 239(PC).
    2. Chen, Jianguo & Han, Xuebing & Sun, Tao & Zheng, Yuejiu, 2024. "Analysis and prediction of battery aging modes based on transfer learning," Applied Energy, Elsevier, vol. 356(C).
    3. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    4. 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).
    5. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
    6. Jiang, Bo & Dai, Haifeng & Wei, Xuezhe, 2020. "Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition," Applied Energy, Elsevier, vol. 269(C).
    7. Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
    8. Qiao, Dongdong & Wang, Xueyuan & Lai, Xin & Zheng, Yuejiu & Wei, Xuezhe & Dai, Haifeng, 2022. "Online quantitative diagnosis of internal short circuit for lithium-ion batteries using incremental capacity method," Energy, Elsevier, vol. 243(C).
    9. Zheng, Yuejiu & Cui, Yifan & Han, Xuebing & Ouyang, Minggao, 2021. "A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method," Energy, Elsevier, vol. 237(C).
    10. Li, Shi & Pischinger, Stefan & He, Chaoyi & Liang, Liliuyuan & Stapelbroek, Michael, 2018. "A comparative study of model-based capacity estimation algorithms in dual estimation frameworks for lithium-ion batteries under an accelerated aging test," Applied Energy, Elsevier, vol. 212(C), pages 1522-1536.
    11. 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.
    12. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
    13. 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).
    14. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    15. 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).
    16. Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    17. Wang, Shuoqi & Guo, Dongxu & Han, Xuebing & Lu, Languang & Sun, Kai & Li, Weihan & Sauer, Dirk Uwe & Ouyang, Minggao, 2020. "Impact of battery degradation models on energy management of a grid-connected DC microgrid," Energy, Elsevier, vol. 207(C).
    18. Jinhyeong Park & Munsu Lee & Gunwoo Kim & Seongyun Park & Jonghoon Kim, 2020. "Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH," Energies, MDPI, vol. 13(9), pages 1-20, April.
    19. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    20. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.

    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:283:y:2023:i:c:s0360544223018510. 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.