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Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network

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  • Xie, Jiale
  • Xu, Jingfan
  • Wei, Zhongbao
  • Li, Xiaoyu

Abstract

Battery-related faults have become the most intractable problem hindering the further prosperity of fields like electric vehicle and grid energy storage. This paper is devoted to constructing a novel diagnostic framework for the faults in series battery packs, resorting to signal imaging and convolutional neural network (CNN) techniques. First, the voltage synchronicity between adjacent cells in a pack is quantified using the recursive correlation coefficient which can percept system anomalies sensitively. Then, reliant on the Gramian Angular Field (GAF) and Markov Transition Field (MTF) transformations, the correlation coefficient series is converted into pseudo images, the textures of which are full of informative details regarding system state. Finally, CNN models are employed to analyze the images for fault symptoms, thereby detecting fault occurrence, inferring fault type and evaluating fault grade. To obtain realistic dataset, different types and severities of faults are physically triggered on a li-ion battery pack. Experimental verification results indicate that the proposed framework can give accurate and reliable judgements on fault specifics, with the accuracy rates of fault type isolating and severity grading as 99.63% and 63.6% on GAF images, and as 99.75% and 58.7% on MTF images, respectively.

Suggested Citation

  • Xie, Jiale & Xu, Jingfan & Wei, Zhongbao & Li, Xiaoyu, 2023. "Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network," Energy, Elsevier, vol. 263(PD).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222027530
    DOI: 10.1016/j.energy.2022.125867
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    References listed on IDEAS

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    1. Xu, Bin & Lee, Jinwoo & Kwon, Daeil & Kong, Lingxi & Pecht, Michael, 2021. "Mitigation strategies for Li-ion battery thermal runaway: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    2. Feng, Fei & Hu, Xiaosong & Hu, Lin & Hu, Fengling & Li, Yang & Zhang, Lei, 2019. "Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 102-113.
    3. Chen, Zeyu & Xiong, Rui & Lu, Jiahuan & Li, Xinggang, 2018. "Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 213(C), pages 375-383.
    4. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    5. Zheng, Changwen & Chen, Ziqiang & Huang, Deyang, 2020. "Fault diagnosis of voltage sensor and current sensor for lithium-ion battery pack using hybrid system modeling and unscented particle filter," Energy, Elsevier, vol. 191(C).
    6. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    7. Zhang, Zehan & Li, Shuanghong & Xiao, Yawen & Yang, Yupu, 2019. "Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning," Applied Energy, Elsevier, vol. 233, pages 930-942.
    8. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    9. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
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