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

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  • 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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    1. Hu, Xiaosong & Murgovski, Nikolce & Johannesson, Lars & Egardt, Bo, 2013. "Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes," Applied Energy, Elsevier, vol. 111(C), pages 1001-1009.
    2. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, November.
    3. Hu, Xiaosong & Li, Shengbo Eben & Jia, Zhenzhong & Egardt, Bo, 2014. "Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles," Energy, Elsevier, vol. 64(C), pages 953-960.
    4. Huan Yang & Yubing Qiu & Xingpeng Guo, 2015. "Prediction of State-of-Health for Nickel-Metal Hydride Batteries by a Curve Model Based on Charge-Discharge Tests," Energies, MDPI, vol. 8(11), pages 1-14, November.
    5. Berecibar, Maitane & Garmendia, Maitane & Gandiaga, Iñigo & Crego, Jon & Villarreal, Igor, 2016. "State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application," Energy, Elsevier, vol. 103(C), pages 784-796.
    6. Zheng, Fangdan & Jiang, Jiuchun & Sun, Bingxiang & Zhang, Weige & Pecht, Michael, 2016. "Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles," Energy, Elsevier, vol. 113(C), pages 64-75.
    7. Li, Xue & Jiang, Jiuchun & Wang, Le Yi & Chen, Dafen & Zhang, Yanru & Zhang, Caiping, 2016. "A capacity model based on charging process for state of health estimation of lithium ion batteries," Applied Energy, Elsevier, vol. 177(C), pages 537-543.
    8. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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    Cited by:

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    3. 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.
    4. 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.
    5. Jiangong Zhu & Yixiu Wang & Yuan Huang & R. Bhushan Gopaluni & Yankai Cao & Michael Heere & Martin J. Mühlbauer & Liuda Mereacre & Haifeng Dai & Xinhua Liu & Anatoliy Senyshyn & Xuezhe Wei & Michael K, 2022. "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. 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.
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