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

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  • 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
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    References listed on IDEAS

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    1. Yang, Chen, 2022. "Running battery electric vehicles with extended range: Coupling cost and energy analysis," Applied Energy, Elsevier, vol. 306(PB).
    2. 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.
    3. 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.
    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).
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    Cited by:

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    3. 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).

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