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Practical failure recognition model of lithium-ion batteries based on partial charging process

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Listed:
  • Chen, Kunlong
  • Zheng, Fangdan
  • Jiang, Jiuchun
  • Zhang, Weige
  • Jiang, Yan
  • Chen, Kunjin

Abstract

The recognition of failed batteries in a battery pack has long been a time-consuming task. Thus there is an urgent need for an on-board method implementation to identify failed batteries for the safe operation of electric vehicles. In this paper, a novel method to identify failed batteries with insufficient capacities is proposed. The properties of the incremental capacity curve are studied. Six features are extracted from the partial incremental capacity curve of each battery and a shrinkage method called the elastic net is used to select two variables that are most relevant to the capacity fade. A classification model based on linear discriminant analysis is established which can assign a given battery into two classes, namely “good” and “bad”. The effect of prior probability for each class of battery and the configuration to minimize true loss are discussed. This proposed method is relatively easy to implement with high accuracy, thus having high practicability.

Suggested Citation

  • Chen, Kunlong & Zheng, Fangdan & Jiang, Jiuchun & Zhang, Weige & Jiang, Yan & Chen, Kunjin, 2017. "Practical failure recognition model of lithium-ion batteries based on partial charging process," Energy, Elsevier, vol. 138(C), pages 1199-1208.
    • Handle: RePEc:eee:energy:v:138:y:2017:i:c:p:1199-1208
    DOI: 10.1016/j.energy.2017.08.017
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    References listed on IDEAS

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    Cited by:

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    3. Zhang, Zhendong & Kong, Xiangdong & Zheng, Yuejiu & Zhou, Long & Lai, Xin, 2019. "Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters," Energy, Elsevier, vol. 166(C), pages 1013-1024.
    4. Chang, Chun & Wang, Qiyue & Jiang, Jiuchun & Jiang, Yan & Wu, Tiezhou, 2023. "Voltage fault diagnosis of a power battery based on wavelet time-frequency diagram," Energy, Elsevier, vol. 278(PB).
    5. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    6. 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.
    7. Cui, Yingzhi & Zuo, Pengjian & Du, Chunyu & Gao, Yunzhi & Yang, Jie & Cheng, Xinqun & Ma, Yulin & Yin, Geping, 2018. "State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method," Energy, Elsevier, vol. 144(C), pages 647-656.
    8. Chen, Xiang & Deng, Yelin & Wang, Xingxing & Yuan, Yinnan, 2024. "The capacity degradation path prediction for the prismatic lithium-ion batteries based on the multi-features extraction with SGPR," Energy, Elsevier, vol. 299(C).
    9. Mona Faraji Niri & Koorosh Aslansefat & Sajedeh Haghi & Mojgan Hashemian & Rüdiger Daub & James Marco, 2023. "A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation," Energies, MDPI, vol. 16(17), pages 1-38, September.

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