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Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles

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  • Tian, Jiaqiang
  • Wang, Yujie
  • Liu, Chang
  • Chen, Zonghai

Abstract

Consistency is an essential factor affecting the operation of lithium-ion battery packs. Pack consistency evaluation is of considerable significance to the usage of batteries. Many existing methods are limited for they are based on a single feature or can only be implemented offline. This paper develops a comprehensive method to evaluate the pack consistency based on multi-feature weighting. Firstly, the features which reflect the static or dynamic characteristics of batteries are excavated. Secondly, a weighted method of multi-feature inconsistency is proposed to evaluate pack consistency. In which case, the entropy weight method is employed to determine the weight. Thirdly, an improved Greenwald-Khanna algorithm based on genetic algorithm and kernel function is developed to cluster batteries. Finally, nine months of electric vehicle data are collated to validate the proposed algorithms. Meanwhile, the main factor affecting consistency change is analyzed. The results show that with the usage of batteries, the difference between the cells becomes more serious, which weakens the pack consistency. Besides, the relationship between the consistency attenuation rate and the driving mileage can be approximated by a first-order function. The higher mileages will aggravate the pack inconsistency. Moreover, it has been proven that the improved clustering algorithm has stronger robustness and classification performance.

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  • Tian, Jiaqiang & Wang, Yujie & Liu, Chang & Chen, Zonghai, 2020. "Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles," Energy, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:energy:v:194:y:2020:i:c:s0360544220300517
    DOI: 10.1016/j.energy.2020.116944
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    References listed on IDEAS

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    Cited by:

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    3. Song, Ziyou & Yang, Niankai & Lin, Xinfan & Pinto Delgado, Fanny & Hofmann, Heath & Sun, Jing, 2022. "Progression of cell-to-cell variation within battery modules under different cooling structures," Applied Energy, Elsevier, vol. 312(C).
    4. Zhang, Junwei & Zhang, Weige & Sun, Bingxiang & Zhang, Yanru & Fan, Xinyuan & Zhao, Bo, 2024. "A novel method of battery pack energy health estimation based on visual feature learning," Energy, Elsevier, vol. 293(C).
    5. Kong, Fanhou & Liang, Xue & Yi, Lanlin & Fang, Xiaohui & Yin, Zhongbin & Wang, Yulong & Zhang, Ruixiang & Liu, Longyang & Chen, Qing & Li, Minghan & Li, Changjiu & Jiang, Hong & Chen, Yongjun, 2021. "Multi-electron reactions for the synthesis of a vanadium-based amorphous material as lithium-ion battery cathode with high specific capacity," Energy, Elsevier, vol. 219(C).
    6. Li, Xiaoyu & Xu, Jianhua & Hong, Jianxun & Tian, Jindong & Tian, Yong, 2021. "State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy," Energy, Elsevier, vol. 214(C).
    7. 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).
    8. Li, Penghua & Liu, Jianfei & Deng, Zhongwei & Yang, Yalian & Lin, Xianke & Couture, Jonathan & Hu, Xiaosong, 2022. "Increasing energy utilization of battery energy storage via active multivariable fusion-driven balancing," Energy, Elsevier, vol. 243(C).
    9. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).

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