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Research on Appearance Detection, Sorting, and Regrouping Technology of Retired Batteries for Electric Vehicles

Author

Listed:
  • Fengdan Liu

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Jiangyi Chen

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Dongchen Qin

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Tingting Wang

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

Abstract

The rapid proliferation of electric vehicle adoption has brought about significant changes in energy consumption patterns, but improper disposal of retired batteries poses new challenges to the environment. In order to promote the sustainable development of the industry using retired batteries, this paper focuses on the research on retired battery appearance detection, sorting, and regrouping technologies. Firstly, the standards for retired battery appearance detection are analyzed, and a method of acquiring battery appearance features through two-stage image acquisition is proposed. Machine vision is employed to achieve the appearance filtering of retired batteries, overcoming the shortcomings of manual screening. For the filtered batteries, capacity, internal resistance, and open-circuit voltage are determined as indicators. Analytic hierarchy process and Gray relation analysis are employed for classification based on four application scenarios. Additionally, an improved Gaussian mixture model clustering algorithm is proposed. In the recombination process, the algorithm parameters are adaptively adjusted for each battery category. Experimental results demonstrate that the accuracy of battery appearance filtering exceeds 97%, and the improved algorithm effectively enhances the consistency among batteries. Compared to the baseline algorithm, the performance consistency of regrouping batteries is increased by more than 5%.

Suggested Citation

  • Fengdan Liu & Jiangyi Chen & Dongchen Qin & Tingting Wang, 2023. "Research on Appearance Detection, Sorting, and Regrouping Technology of Retired Batteries for Electric Vehicles," Sustainability, MDPI, vol. 15(21), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15523-:d:1272409
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    References listed on IDEAS

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    1. Hwang, Jungbin, 2021. "Simple and trustworthy cluster-robust GMM inference," Journal of Econometrics, Elsevier, vol. 222(2), pages 993-1023.
    2. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
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