IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i21p15523-d1272409.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/21/15523/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/21/15523/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Hwang, Jungbin, 2021. "Simple and trustworthy cluster-robust GMM inference," Journal of Econometrics, Elsevier, vol. 222(2), pages 993-1023.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Doan, Anh-Tuan & Lin, Kun-Li, 2022. "Bank ownership and stock price informativeness. Does politics matter?," International Review of Financial Analysis, Elsevier, vol. 79(C).
    2. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    3. Rustam Ibragimov & Paul Kattuman & Anton Skrobotov, 2021. "Robust Inference on Income Inequality: $t$-Statistic Based Approaches," Papers 2105.05335, arXiv.org, revised Nov 2021.
    4. Zheng, Jianfei & Ren, Jincheng & Zhang, Jianxun & Pei, Hong & Zhang, Zhengxin, 2023. "A lifetime prediction method for Lithium-ion batteries considering storage degradation of spare parts," Energy, Elsevier, vol. 282(C).
    5. Farhad Salek & Shahaboddin Resalati & Aydin Azizi & Meisam Babaie & Paul Henshall & Denise Morrey, 2024. "State of Health Prediction of Electric Vehicles’ Retired Batteries Based on First-Life Historical Degradation Data Using Predictive Time-Series Algorithms," Mathematics, MDPI, vol. 12(7), pages 1-20, March.
    6. Tang, Aihua & Jiang, Yihan & Nie, Yuwei & Yu, Quanqing & Shen, Weixiang & Pecht, Michael G., 2023. "Health and lifespan prediction considering degradation patterns of lithium-ion batteries based on transferable attention neural network," Energy, Elsevier, vol. 279(C).
    7. Khan, Noman & Khan, Samee Ullah & Baik, Sung Wook, 2023. "Deep dive into hybrid networks: A comparative study and novel architecture for efficient power prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    8. Paweł Kut & Katarzyna Pietrucha-Urbanik & Martina Zeleňáková, 2024. "Assessing the Role of Hydrogen in Sustainable Energy Futures: A Comprehensive Bibliometric Analysis of Research and International Collaborations in Energy and Environmental Engineering," Energies, MDPI, vol. 17(8), pages 1-27, April.
    9. Valentin Courgeau & Almut E.D. Veraart, 2022. "Asymptotic theory for the inference of the latent trawl model for extreme values," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1448-1495, December.
    10. Xue, Jingsong & Ma, Wentao & Feng, Xiaoyang & Guo, Peng & Guo, Yaosong & Hu, Xianzhi & Chen, Badong, 2023. "Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 284(C).
    11. Guo, Junyu & Wan, Jia-Lun & Yang, Yan & Dai, Le & Tang, Aimin & Huang, Bangkui & Zhang, Fangfang & Li, He, 2023. "A deep feature learning method for remaining useful life prediction of drilling pumps," Energy, Elsevier, vol. 282(C).
    12. Hwang, Jungbin & Valdés, Gonzalo, 2023. "Finite-sample corrected inference for two-step GMM in time series," Journal of Econometrics, Elsevier, vol. 234(1), pages 327-352.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15523-:d:1272409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.