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Using Self Organizing Maps to Achieve Lithium-Ion Battery Cells Multi-Parameter Sorting Based on Principle Components Analysis

Author

Listed:
  • Bizhong Xia

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Yadi Yang

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Jie Zhou

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Guanghao Chen

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Yifan Liu

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China)

  • Huawen Wang

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, China)

  • Mingwang Wang

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, China)

  • Yongzhi Lai

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, China)

Abstract

Battery sorting is an important process in the production of lithium battery module and battery pack for electric vehicles (EVs). Accurate battery sorting can ensure good consistency of batteries for grouping. This study investigates the mechanism of inconsistency of battery packs and process of battery sorting on the lithium-ion battery module production line. Combined with the static and dynamic characteristics of lithium-ion batteries, the battery parameters on the production line that can be used as a sorting basis are analyzed, and the parameters of battery mass, volume, resistance, voltage, charge/discharge capacity and impedance characteristics are measured. The data of batteries are processed by the principal component analysis (PCA) method in statistics, and after analysis, the parameters of batteries are obtained. Principal components are used as sorting variables, and the self-organizing map (SOM) neural network is carried out to cluster the batteries. Group experiments are carried out on the separated batteries, and state of charge (SOC) consistency of the batteries is achieved to verify that the sorting algorithm and sorting result is accurate.

Suggested Citation

  • Bizhong Xia & Yadi Yang & Jie Zhou & Guanghao Chen & Yifan Liu & Huawen Wang & Mingwang Wang & Yongzhi Lai, 2019. "Using Self Organizing Maps to Achieve Lithium-Ion Battery Cells Multi-Parameter Sorting Based on Principle Components Analysis," Energies, MDPI, vol. 12(15), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2980-:d:254054
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    References listed on IDEAS

    as
    1. Kim, Jonghoon & Cho, B.H., 2013. "Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation," Energy, Elsevier, vol. 57(C), pages 581-599.
    2. Bizhong Xia & Shengkun Guo & Wei Wang & Yongzhi Lai & Huawen Wang & Mingwang Wang & Weiwei Zheng, 2018. "A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries," Energies, MDPI, vol. 11(10), pages 1-15, October.
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    Cited by:

    1. Ali, Hayder & Khan, Hassan Abbas & Pecht, Michael, 2022. "Preprocessing of spent lithium-ion batteries for recycling: Need, methods, and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Tian, Jiaqiang & Fan, Yuan & Pan, Tianhong & Zhang, Xu & Yin, Jianning & Zhang, Qingping, 2024. "A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    3. Hongwei Tang & Anping Lin & Wei Sun & Shuqi Shi, 2020. "An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments," Energies, MDPI, vol. 13(12), pages 1-18, June.

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