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A Robust Battery Grouping Method Based on a Characteristic Distribution Model

Author

Listed:
  • Yuxiang Yang

    (Department of Electronics and Information, Hangzhou Dianzi University, 2nd Street, Xiasha Higher Education Zone, Hangzhou 310018, China)

  • Mingyu Gao

    (Department of Electronics and Information, Hangzhou Dianzi University, 2nd Street, Xiasha Higher Education Zone, Hangzhou 310018, China)

  • Zhiwei He

    (Department of Electronics and Information, Hangzhou Dianzi University, 2nd Street, Xiasha Higher Education Zone, Hangzhou 310018, China)

  • Caisheng Wang

    (Departmant of Electric and Computer Engineering, Wayne State University, 5050 Anthony Wayne Drive, Detroit, MI 48202, USA)

Abstract

The inconsistent characteristics of individual power batteries in a battery pack can seriously affect the performance and service life of the whole pack. Battery grouping is an effective approach for dealing with the inconsistency problem by grouping batteries with similar characteristics in the same battery pack. In actual production, the battery grouping process still relies on the traditional manual method, which results in high labor and time costs. In this paper, a robust and effective battery grouping method based on the characteristic distribution model is developed. Specifically, a novel characteristic distribution model is proposed to determine the grouping priority of different batteries. Then, an improved k-nearest-neighbor algorithm is used to decide which batteries should be group into the same battery pack. Experimental results demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Yuxiang Yang & Mingyu Gao & Zhiwei He & Caisheng Wang, 2017. "A Robust Battery Grouping Method Based on a Characteristic Distribution Model," Energies, MDPI, vol. 10(7), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1035-:d:105253
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    References listed on IDEAS

    as
    1. Zhiwei He & Mingyu Gao & Caisheng Wang & Leyi Wang & Yuanyuan Liu, 2013. "Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model," Energies, MDPI, vol. 6(8), pages 1-18, August.
    2. Wang, Tao & Tseng, K.J. & Zhao, Jiyun & Wei, Zhongbao, 2014. "Thermal investigation of lithium-ion battery module with different cell arrangement structures and forced air-cooling strategies," Applied Energy, Elsevier, vol. 134(C), pages 229-238.
    3. Zhiwei He & Mingyu Gao & Guojin Ma & Yuanyuan Liu & Lijun Tang, 2016. "Battery Grouping with Time Series Clustering Based on Affinity Propagation," Energies, MDPI, vol. 9(7), pages 1-11, July.
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