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Residual Performance Evaluation of Electric Vehicle Batteries: Focusing on the Analysis Results of a Social Survey of Vehicle Owners

Author

Listed:
  • Hongxia Chen

    (Graduate School of International Cultural Studies, Tohoku University, Sendai 980-8576, Japan)

  • Xiaoyue Liu

    (Graduate School of International Cultural Studies, Tohoku University, Sendai 980-8576, Japan)

  • Jeongsoo Yu

    (Graduate School of International Cultural Studies, Tohoku University, Sendai 980-8576, Japan)

  • Kazuaki Okubo

    (Graduate School of International Cultural Studies, Tohoku University, Sendai 980-8576, Japan)

Abstract

In recent years, electric vehicles (EVs) have attracted much attention worldwide as an effective solution for realizing a carbon-neutral and decarbonized society. Since many batteries are generated over the lifespan of EVs, battery recycling has become an important issue. However, battery-manufacturing countries, including China, Japan, and South Korea (CJK), have yet to build a complete battery resource recovery system. In particular, owing to the lack of a reliable battery performance evaluation method, the residual performance of a battery cannot be accurately determined. Thus, based on the results of a social survey of EV owners, the present study develops a novel battery residual performance evaluation method that can easily predict the remaining battery performance based on the usage status of the vehicle, without relying on external electronic devices to measure the battery parameters. In addition, by clarifying the human factors deteriorating vehicle battery performance and proposing sustainable utilization methods for EVs, the present study demonstrates important research prospects for the protection of the environment and progress in the automobile industry.

Suggested Citation

  • Hongxia Chen & Xiaoyue Liu & Jeongsoo Yu & Kazuaki Okubo, 2025. "Residual Performance Evaluation of Electric Vehicle Batteries: Focusing on the Analysis Results of a Social Survey of Vehicle Owners," Sustainability, MDPI, vol. 17(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:10:p:4685-:d:1659747
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    References listed on IDEAS

    as
    1. Li, Renzheng & Hong, Jichao & Zhang, Huaqin & Chen, Xinbo, 2022. "Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles," Energy, Elsevier, vol. 257(C).
    2. Yang, Fangfang & Song, Xiangbao & Dong, Guangzhong & Tsui, Kwok-Leung, 2019. "A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries," Energy, Elsevier, vol. 171(C), pages 1173-1182.
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