IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v243y2022ics0360544221031315.html
   My bibliography  Save this article

A novel capacity and initial discharge electric quantity estimation method for LiFePO4 battery pack based on OCV curve partial reconstruction

Author

Listed:
  • Sun, Jinlei
  • Tang, Yong
  • Ye, Jilei
  • Jiang, Tao
  • Chen, Saihan
  • Qiu, Shengshi

Abstract

LiFePO4 batteries are widely used in electric vehicles (EVs) and battery energy storage systems (BESSs). However, the consistencies in capacity, internal resistance, and open circuit voltage (OCV) limit the battery pack performance, which makes it difficult to evaluate the state information of the cells effectively without disassembling the pack. Therefore, this paper proposes a battery capacity and initial discharge electric quantity (DEQ) estimation method for series-connected battery packs based on partial reconstruction of the OCV curve. The aging characteristics of each cell are extracted based on incremental capacity analysis (ICA), which is taken as the transformation law for the OCV curve considering aging. And then the multipopulation genetic algorithm (MPGA) is utilized to optimize the transformation coefficients and obtain the reconstructed OCV curve of each cell in the pack. Finally, the actual capacity and initial DEQ of batteries at a low current rate are estimated according to the reconstructed complete OCV curve and optimized OCV curve transformation coefficients. The experimental results show that the estimation error of capacity is less than 5%, and the maximum estimation error of the initial DEQ is 5.56%.

Suggested Citation

  • Sun, Jinlei & Tang, Yong & Ye, Jilei & Jiang, Tao & Chen, Saihan & Qiu, Shengshi, 2022. "A novel capacity and initial discharge electric quantity estimation method for LiFePO4 battery pack based on OCV curve partial reconstruction," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221031315
    DOI: 10.1016/j.energy.2021.122882
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221031315
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122882?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. 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).
    2. Li, Junfu & Wang, Lixin & Lyu, Chao & Zhang, Liqiang & Wang, Han, 2015. "Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions," Energy, Elsevier, vol. 86(C), pages 638-648.
    3. Zengkai Wang & Shengkui Zeng & Jianbin Guo & Taichun Qin, 2018. "Remaining capacity estimation of lithium-ion batteries based on the constant voltage charging profile," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-22, July.
    4. 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).
    5. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
    6. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    7. Li, Xiaoyu & Wang, Zhenpo & Zhang, Lei, 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 174(C), pages 33-44.
    8. Zhang, Caiping & Jiang, Yan & Jiang, Jiuchun & Cheng, Gong & Diao, Weiping & Zhang, Weige, 2017. "Study on battery pack consistency evolutions and equilibrium diagnosis for serial- connected lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 510-519.
    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. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).
    2. 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).

    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. 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).
    2. 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).
    3. Yang, Jufeng & Cai, Yingfeng & Mi, Chris, 2022. "Lithium-ion battery capacity estimation based on battery surface temperature change under constant-current charge scenario," Energy, Elsevier, vol. 241(C).
    4. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
    5. S. Tamilselvi & S. Gunasundari & N. Karuppiah & Abdul Razak RK & S. Madhusudan & Vikas Madhav Nagarajan & T. Sathish & Mohammed Zubair M. Shamim & C. Ahamed Saleel & Asif Afzal, 2021. "A Review on Battery Modelling Techniques," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    6. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Ma, Junpeng & Luo, Guangzhao & Teodorescu, Remus, 2020. "An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system," Energy, Elsevier, vol. 206(C).
    7. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    8. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
    9. 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).
    10. Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
    11. Liu, Chenghao & Deng, Zhongwei & Zhang, Xiaohong & Bao, Huanhuan & Cheng, Duanqian, 2024. "Battery state of health estimation across electrochemistry and working conditions based on domain adaptation," Energy, Elsevier, vol. 297(C).
    12. Jiang, Bo & Zhu, Yuli & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range," Energy, Elsevier, vol. 263(PC).
    13. Qian, Cheng & Xu, Binghui & Chang, Liang & Sun, Bo & Feng, Qiang & Yang, Dezhen & Ren, Yi & Wang, Zili, 2021. "Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries," Energy, Elsevier, vol. 227(C).
    14. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    15. 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).
    16. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    17. Fan, Guodong & Zhang, Xi, 2023. "Battery capacity estimation using 10-second relaxation voltage and a convolutional neural network," Applied Energy, Elsevier, vol. 330(PA).
    18. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    19. Singh, Karanjot & Tjahjowidodo, Tegoeh & Boulon, Loïc & Feroskhan, Mir, 2022. "Framework for measurement of battery state-of-health (resistance) integrating overpotential effects and entropy changes using energy equilibrium," Energy, Elsevier, vol. 239(PA).
    20. 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).

    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:243:y:2022:i:c:s0360544221031315. 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.