IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v327y2022ics0306261922012831.html
   My bibliography  Save this article

Degradation characteristics investigation for lithium-ion cells with NCA cathode during overcharging

Author

Listed:
  • Zhang, Lei
  • Huang, Lvwei
  • Zhang, Zhaosheng
  • Wang, Zhenpo
  • Dorrell, David D.

Abstract

This paper explores the major degradation characteristics of commercial lithium-ion battery cells with nickel–cobalt-aluminum-oxide (NCA) electrode during cyclic overcharging, and proposes non-destructive methods for detecting overcharging degradation failure. The experimental results show that battery capacity drops significantly with increasing overcharge depth and number of cycles especially during the first three cycles and when the charging termination voltage is set to 5 V. At the same time, the cell overcharge tolerance decreases with the cyclic overcharging. The combination of the electrochemical impedance spectroscopy and the incremental capacity and differential voltage analysis is used to diagnose cell degradation during cyclic overcharging. Three main degradation modes are identified and quantified by extracting characteristic parameters such as internal resistance and peak, valley, and curve position changes of incremental capacity curves. It is concluded that loss of lithium inventory and loss of active materials are the most dominant degradation modes during cyclic overcharging. Besides, the sharp increase of the third peak on incremental capacity curves has been identified as a unique feature of overcharging degradation, which can be used for diagnosing cyclic overcharging-induced degradation for batteries with NCA cathode.

Suggested Citation

  • Zhang, Lei & Huang, Lvwei & Zhang, Zhaosheng & Wang, Zhenpo & Dorrell, David D., 2022. "Degradation characteristics investigation for lithium-ion cells with NCA cathode during overcharging," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922012831
    DOI: 10.1016/j.apenergy.2022.120026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922012831
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120026?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. 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.
    2. 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.
    3. Yang, Chen, 2022. "Running battery electric vehicles with extended range: Coupling cost and energy analysis," Applied Energy, Elsevier, vol. 306(PB).
    4. Zhu, Xiaoqing & Wang, Zhenpo & Wang, Yituo & Wang, Hsin & Wang, Cong & Tong, Lei & Yi, Mi, 2019. "Overcharge investigation of large format lithium-ion pouch cells with Li(Ni0.6Co0.2Mn0.2)O2 cathode for electric vehicles: Thermal runaway features and safety management method," Energy, Elsevier, vol. 169(C), pages 868-880.
    5. Chen, Jie & Ren, Dongsheng & Hsu, Hungjen & Wang, Li & He, Xiangming & Zhang, Caiping & Feng, Xuning & Ouyang, Minggao, 2021. "Investigating the thermal runaway features of lithium-ion batteries using a thermal resistance network model," Applied Energy, Elsevier, vol. 295(C).
    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. 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).
    2. Li, Da & Deng, Junjun & Zhang, Zhaosheng & Liu, Peng & Wang, Zhenpo, 2023. "Multi-dimension statistical analysis and selection of safety-representing features for battery pack in real-world electric vehicles," Applied Energy, Elsevier, vol. 343(C).
    3. Liu, Jiong & Ma, Zhichao & Guo, Zixin & Zhao, Wenyang & Wang, Shenghui & Zhao, Hongwei & Ren, Luquan, 2024. "Experimental investigation on mechanical-electrochemical coupling properties of cylindrical lithium-ion batteries," Energy, Elsevier, vol. 293(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. Li, Xiaoyu & Zhang, Zuguang & Wang, Wenhui & Tian, Yong & Li, Dong & Tian, Jindong, 2020. "Multiphysical field measurement and fusion for battery electric-thermal-contour performance analysis," Applied Energy, Elsevier, vol. 262(C).
    2. Wang, Cong-jie & Zhu, Yan-li & Gao, Fei & Bu, Xin-ya & Chen, Heng-shuai & Quan, Ting & Xu, Yi-bo & Jiao, Qing-jie, 2022. "Internal short circuit and thermal runaway evolution mechanism of fresh and retired lithium-ion batteries with LiFePO4 cathode during overcharge," Applied Energy, Elsevier, vol. 328(C).
    3. 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.
    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. 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).
    6. 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).
    7. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).
    8. Zhang, Yue & Song, Laifeng & Tian, Jiamin & Mei, Wenxin & Jiang, Lihua & Sun, Jinhua & Wang, Qingsong, 2024. "Modeling the propagation of internal thermal runaway in lithium-ion battery," Applied Energy, Elsevier, vol. 362(C).
    9. Hou, Liubin & Dong, Ao & Ma, Ruifei & Lin, Hejie & Deng, Yelin, 2024. "The sensitive detection of the early-stage internal short circuit triggered by lithium plating through the simplified electrochemical model at various working conditions," Energy, Elsevier, vol. 304(C).
    10. 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).
    11. Guo, Feng & Hu, Guangdi & Xiang, Shun & Zhou, Pengkai & Hong, Ru & Xiong, Neng, 2019. "A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters," Energy, Elsevier, vol. 178(C), pages 79-88.
    12. Zhang, Shuzhi & Zhang, Chen & Jiang, Shiyong & Zhang, Xiongwen, 2022. "A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation," Energy, Elsevier, vol. 246(C).
    13. Gharehghani, Ayat & Rabiei, Moeed & Mehranfar, Sadegh & Saeedipour, Soheil & Mahmoudzadeh Andwari, Amin & García, Antonio & Reche, Carlos Mico, 2024. "Progress in battery thermal management systems technologies for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    14. Hasan Huseyin Coban & Wojciech Lewicki & Ewelina Sendek-Matysiak & Zbigniew Łosiewicz & Wojciech Drożdż & Radosław Miśkiewicz, 2022. "Electric Vehicles and Vehicle–Grid Interaction in the Turkish Electricity System," Energies, MDPI, vol. 15(21), pages 1-19, November.
    15. 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.
    16. Li, Da & Zhang, Zhaosheng & Zhou, Litao & Liu, Peng & Wang, Zhenpo & Deng, Junjun, 2022. "Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction," Applied Energy, Elsevier, vol. 325(C).
    17. 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).
    18. 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).
    19. Marcel Roy B. Domalanta & Julie Anne D. R. Paraggua, 2023. "A Multiphysics Model Simulating the Electrochemical, Thermal, and Thermal Runaway Behaviors of Lithium Polymer Battery," Energies, MDPI, vol. 16(6), pages 1-24, March.
    20. Liu, Yanhui & Zhang, Lei & Ding, Yifei & Huang, Xianjia & Huang, Xinyan, 2024. "Effect of thermal impact on the onset and propagation of thermal runaway over cylindrical Li-ion batteries," Renewable Energy, Elsevier, vol. 222(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:appene:v:327:y:2022:i:c:s0306261922012831. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.