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Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review

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  • Lai, Xin
  • Huang, Yunfeng
  • Deng, Cong
  • Gu, Huanghui
  • Han, Xuebing
  • Zheng, Yuejiu
  • Ouyang, Minggao

Abstract

With the rapid development of electric vehicles, the safe and environmentally friendly disposal of retired lithium batteries (LIBs) is becoming a serious issue. Echelon utilization of the retired LIBs is a promising scheme because of its considerable potential for generating economic and environmental value. The most outstanding technical challenge of echelon utilization is how to sort and regroup the large-scale retired LIBs efficiently and accurately. In this paper, the status and challenges of echelon utilization for the retired LIBs are reviewed. First, the criteria, policies, regulations, markets, costs, and values of echelon utilization are summarized comprehensively to illustrate its potential and expose existing problems and pain points. Second, the key technologies related to large-scale echelon utilization of LIBs are detailed; valuable opinions and technical routes are presented for the selection and rapid estimation of sorting indices, the classification and regrouping algorithm, evaluation of the sorting results, and other aspects. In particular, a multilevel and multidimensional fast sorting method is proposed for large-scale echelon utilization of retired LIBs that considers different scenario constraints. Valuable solutions to the key technical problems are given, such as predicting the characteristics of retired LIBs with in-service data and building a fast sorting model from a small number of samples to sort large quantities of LIBs. Finally, the technological prospects of echelon utilization are discussed. Big data and artificial intelligence can be used to promote further development and application of echelon utilization, which may eventually be applied to managing the whole life cycle of LIBs.

Suggested Citation

  • Lai, Xin & Huang, Yunfeng & Deng, Cong & Gu, Huanghui & Han, Xuebing & Zheng, Yuejiu & Ouyang, Minggao, 2021. "Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
  • Handle: RePEc:eee:rensus:v:146:y:2021:i:c:s1364032121004512
    DOI: 10.1016/j.rser.2021.111162
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    Cited by:

    1. Ali, Hayder & Khan, Hassan Abbas & Pecht, Michael, 2022. "Preprocessing of spent lithium-ion batteries for recycling: Need, methods, and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Ma, Chen & Chang, Long & Cui, Naxin & Duan, Bin & Zhang, Yulong & Yu, Zhihao, 2022. "Statistical relationships between numerous retired lithium-ion cells and packs with random sampling for echelon utilization," Energy, Elsevier, vol. 257(C).
    3. Lu, Jiajia & Zhang, Yanqiong & Huang, Weiwei & Omran, Mamdouh & Zhang, Fan & Gao, Lei & Chen, Guo, 2023. "Reductive roasting of cathode powder of spent ternary lithium-ion battery by pyrolysis of invasive plant Crofton weed," Renewable Energy, Elsevier, vol. 206(C), pages 86-96.
    4. Aree Wangsupphaphol & Surachai Chaitusaney & Mohamed Salem, 2023. "A Techno-Economic Assessment of a Second-Life Battery and Photovoltaics Hybrid Power Source for Sustainable Electric Vehicle Home Charging," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    5. Zhang, Huiming & Zhu, Kexin & Hang, Zixuan & Zhou, Dequn & Zhou, Yi & Xu, Zhidong, 2022. "Waste battery-to-reutilization decisions under government subsidies: An evolutionary game approach," Energy, Elsevier, vol. 259(C).
    6. Wang, Mengmeng & Liu, Kang & Dutta, Shanta & Alessi, Daniel S. & Rinklebe, Jörg & Ok, Yong Sik & Tsang, Daniel C.W., 2022. "Recycling of lithium iron phosphate batteries: Status, technologies, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    7. Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
    8. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    9. Shengyu Tao & Haizhou Liu & Chongbo Sun & Haocheng Ji & Guanjun Ji & Zhiyuan Han & Runhua Gao & Jun Ma & Ruifei Ma & Yuou Chen & Shiyi Fu & Yu Wang & Yaojie Sun & Yu Rong & Xuan Zhang & Guangmin Zhou , 2023. "Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    10. Yongyou Nie & Yuhan Wang & Lu Li & Haolan Liao, 2023. "Literature Review on Power Battery Echelon Reuse and Recycling from a Circular Economy Perspective," IJERPH, MDPI, vol. 20(5), pages 1-28, February.

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