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A Novel Voltage-Abnormal Cell Detection Method for Lithium-Ion Battery Mass Production Based on Data-Driven Model with Multi-Source Time Series Data

Author

Listed:
  • Xiang Wang

    (The School of Automation, Central South University, Changsha 410038, China)

  • Jianjun He

    (The School of Automation, Central South University, Changsha 410038, China)

  • Fuxin Huang

    (The School of Automation, Central South University, Changsha 410038, China)

  • Zhenjie Liu

    (The School of Automation, Central South University, Changsha 410038, China)

  • Aibin Deng

    (The School of Automation, Central South University, Changsha 410038, China)

  • Rihui Long

    (The School of Automation, Central South University, Changsha 410038, China)

Abstract

Before leaving the factory, lithium-ion battery (LIB) cells are screened to exclude voltage-abnormal cells, which can increase the fault rate, troubleshooting difficulty, and degrade pack performance. However, the time interval to obtain the detection results through the existing voltage-abnormal cell method is too long, which can seriously affect production efficiency and delay shipment, especially in the mass production of LIBs when facing a large number of time-critical orders. In this paper, we propose a data-driven voltage-abnormal cell detection method, using a fast model with simple architecture, which can detect voltage-abnormal cells based on the multi-source time series data of the LIB without a time interval. Firstly, our method transforms the different source data of a cell into a multi-source time series data representation and utilizes a recurrent-based data embedding to model the relation within it. Then, a simplified MobileNet is used to extract hidden feature from the embedded data. Finally, we detect the voltage-abnormal cells according to the hidden feature with a cell classification head. The experiment results show that the accuracy and average running time of our model on the voltage-abnormal cell detection task is 95.42% and 0.0509 ms per sample, which is a considerable improvement over existing methods.

Suggested Citation

  • Xiang Wang & Jianjun He & Fuxin Huang & Zhenjie Liu & Aibin Deng & Rihui Long, 2024. "A Novel Voltage-Abnormal Cell Detection Method for Lithium-Ion Battery Mass Production Based on Data-Driven Model with Multi-Source Time Series Data," Energies, MDPI, vol. 17(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3472-:d:1435148
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    References listed on IDEAS

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    1. Coester, Andreas & Hofkes, Marjan W. & Papyrakis, Elissaios, 2020. "Economic analysis of batteries: Impact on security of electricity supply and renewable energy expansion in Germany," Applied Energy, Elsevier, vol. 275(C).
    2. Chengbao Liu & Jie Tan & Xuelei Wang, 2020. "A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 833-845, April.
    3. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
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