IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v257y2022ics036054422201595x.html
   My bibliography  Save this article

Statistical relationships between numerous retired lithium-ion cells and packs with random sampling for echelon utilization

Author

Listed:
  • Ma, Chen
  • Chang, Long
  • Cui, Naxin
  • Duan, Bin
  • Zhang, Yulong
  • Yu, Zhihao

Abstract

Retired batteries are widely repurposed in energy storage packs as an economical and eco-friendly method to achieve echelon utilization. However, pack performance is strongly affected by variations in retired cells and pack configuration. Quantifying this effect in various pack configurations considering cell-to-cell variations is crucial for predicting the performance of numerous packs. Therefore, under a random sampling scenario, we developed statistical models of relationships between retired cells and packs in terms of capacity and resistance based on probability and statistics, thereby providing a solid theoretical foundation for designing and optimizing the pack structure. It is proven that parallel configuration improves the utilization efficiency and variation of pack-level capacities. Meanwhile, both parallel and series configurations reduce the pack-level resistance variation. Moreover, the statistical capacity performance of packs with parallel connections in series is superior to that of packs with series connections in parallel, although their statistical resistance characteristics are the same. Furthermore, based on the developed models, a capacity screening criterion is proposed that retired cells with a capacity greater than μC-2σC should be accepted in screening process to randomly compose energy storage packs, thereby reducing the capacity variation of packs while making full use of retired cells.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:257:y:2022:i:c:s036054422201595x
    DOI: 10.1016/j.energy.2022.124692
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422201595X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124692?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. Steckel, Tobiah & Kendall, Alissa & Ambrose, Hanjiro, 2021. "Applying levelized cost of storage methodology to utility-scale second-life lithium-ion battery energy storage systems," Applied Energy, Elsevier, vol. 300(C).
    2. Zhong, Liang & Zhang, Chenbin & He, Yao & Chen, Zonghai, 2014. "A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis," Applied Energy, Elsevier, vol. 113(C), pages 558-564.
    3. Rothgang, Susanne & Baumhöfer, Thorsten & van Hoek, Hauke & Lange, Tobias & De Doncker, Rik W. & Sauer, Dirk Uwe, 2015. "Modular battery design for reliable, flexible and multi-technology energy storage systems," Applied Energy, Elsevier, vol. 137(C), pages 931-937.
    4. Zhang, Youlang & Li, Yan & Tao, Yibin & Ye, Jilei & Pan, Aiqiang & Li, Xinzhou & Liao, Qiangqiang & Wang, Zhiqin, 2020. "Performance assessment of retired EV battery modules for echelon use," Energy, Elsevier, vol. 193(C).
    5. Rogers, Daniel J. & Aslett, Louis J.M. & Troffaes, Matthias C.M., 2021. "Modelling of modular battery systems under cell capacity variation and degradation," Applied Energy, Elsevier, vol. 283(C).
    6. 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).
    7. 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.
    8. Yang Yang & Wenchao Zhu & Changjun Xie & Ying Shi & Furong Liu & Weibo Li & Zebo Tang, 2020. "A Layered Bidirectional Active Equalization Method for Retired Power Lithium-Ion Batteries for Energy Storage Applications," Energies, MDPI, vol. 13(4), pages 1-15, February.
    9. Zongwei Liu & Xinglong Liu & Han Hao & Fuquan Zhao & Amer Ahmad Amer & Hassan Babiker, 2020. "Research on the Critical Issues for Power Battery Reusing of New Energy Vehicles in China," Energies, MDPI, vol. 13(8), pages 1-19, April.
    10. Lai, Xin & Huang, Yunfeng & Deng, Cong & Gu, Huanghui & Han, Xuebing & Zheng, Yuejiu & Ouyang, Minggao, 2021. "Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    11. Fabian Duffner & Niklas Kronemeyer & Jens Tübke & Jens Leker & Martin Winter & Richard Schmuch, 2021. "Post-lithium-ion battery cell production and its compatibility with lithium-ion cell production infrastructure," Nature Energy, Nature, vol. 6(2), pages 123-134, February.
    12. Jiang, Yan & Jiang, Jiuchun & Zhang, Caiping & Zhang, Weige & Gao, Yang & Mi, Chris, 2019. "A Copula-based battery pack consistency modeling method and its application on the energy utilization efficiency estimation," Energy, Elsevier, vol. 189(C).
    13. Astaneh, Majid & Andric, Jelena & Löfdahl, Lennart & Stopp, Peter, 2022. "Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications," Energy, Elsevier, vol. 239(PB).
    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. Gu, Pingwei & Zhang, Ying & Duan, Bin & Zhang, Chenghui & Kang, Yongzhe, 2024. "Rapid and flexible lithium-ion battery performance evaluation using random charging curve based on deep learning," Energy, Elsevier, vol. 293(C).
    2. Gu, Xin & Li, Jinglun & Zhu, Yuhao & Wang, Yue & Mao, Ziheng & Shang, Yunlong, 2023. "A quick and intelligent screening method for large-scale retired batteries based on cloud-edge collaborative architecture," Energy, Elsevier, vol. 285(C).
    3. Chang, Long & Ma, Chen & Zhang, Chenghui & Duan, Bin & Cui, Naxin & Li, Changlong, 2023. "Correlations of lithium-ion battery parameter variations and connected configurations on pack statistics," Applied Energy, Elsevier, vol. 329(C).
    4. Achim Kampker & Heiner Hans Heimes & Christian Offermanns & Merlin Frank & Domenic Klohs & Khanh Nguyen, 2023. "Prediction of Battery Return Volumes for 3R: Remanufacturing, Reuse, and Recycling," Energies, MDPI, vol. 16(19), pages 1-22, September.

    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. Chang, Long & Ma, Chen & Zhang, Chenghui & Duan, Bin & Cui, Naxin & Li, Changlong, 2023. "Correlations of lithium-ion battery parameter variations and connected configurations on pack statistics," Applied Energy, Elsevier, vol. 329(C).
    2. 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).
    3. 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).
    4. 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).
    5. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    6. 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).
    7. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    8. He, Xitian & Sun, Bingxiang & Zhang, Weige & Su, Xiaojia & Ma, Shichang & Li, Hao & Ruan, Haijun, 2023. "Inconsistency modeling of lithium-ion battery pack based on variational auto-encoder considering multi-parameter correlation," Energy, Elsevier, vol. 277(C).
    9. 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).
    10. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    11. Bingxiang Sun & Xinze Zhao & Xitian He & Haijun Ruan & Zhenlin Zhu & Xingzhen Zhou, 2023. "Virtual Battery Pack-Based Battery Management System Testing Framework," Energies, MDPI, vol. 16(2), pages 1-21, January.
    12. Braco, Elisa & San Martín, Idoia & Sanchis, Pablo & Ursúa, Alfredo & Stroe, Daniel-Ioan, 2022. "State of health estimation of second-life lithium-ion batteries under real profile operation," Applied Energy, Elsevier, vol. 326(C).
    13. Gu, Pingwei & Zhang, Ying & Duan, Bin & Zhang, Chenghui & Kang, Yongzhe, 2024. "Rapid and flexible lithium-ion battery performance evaluation using random charging curve based on deep learning," Energy, Elsevier, vol. 293(C).
    14. 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).
    15. Liu, Xinhua & Ai, Weilong & Naylor Marlow, Max & Patel, Yatish & Wu, Billy, 2019. "The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs," Applied Energy, Elsevier, vol. 248(C), pages 489-499.
    16. Fan, Wenjun & Zhu, Jiangong & Qiao, Dongdong & Jiang, Bo & Wang, Xueyuan & Wei, Xuezhe & Dai, Haifeng, 2024. "Prediction of nonlinear degradation knee-point and remaining useful life for lithium-ion batteries using relaxation voltage," Energy, Elsevier, vol. 294(C).
    17. Zhang, Chenxi & Yang, Yi & Wang, Yunqi & Qiu, Jing & Zhao, Junhua, 2024. "Auction-based peer-to-peer energy trading considering echelon utilization of retired electric vehicle second-life batteries," Applied Energy, Elsevier, vol. 358(C).
    18. 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).
    19. Roman Gozdur & Tomasz Przerywacz & Dariusz Bogdański, 2021. "Low Power Modular Battery Management System with a Wireless Communication Interface," Energies, MDPI, vol. 14(19), pages 1-20, October.
    20. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.

    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:257:y:2022:i:c:s036054422201595x. 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.