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A quick and intelligent screening method for large-scale retired batteries based on cloud-edge collaborative architecture

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  • Gu, Xin
  • Li, Jinglun
  • Zhu, Yuhao
  • Wang, Yue
  • Mao, Ziheng
  • Shang, Yunlong

Abstract

Rapid and accurate sorting consistent cells is indispensable for the cascade utilization of retired batteries. However, the conventional full charge-discharge capacity testing methods are time-consuming and low efficient, which cannot be employed to screen large-scale retired batteries. Moreover, most existing artificial intelligence techniques sort retired batteries at cloud servers, which leads to tremendous burdens of transmission. Therefore, a fast and intelligent screening approach based on a Light Gradient Boosting Machine is proposed, which only utilizes partial charge/discharge curves, significantly reducing the test time. Meanwhile, a cloud-edge collaborative framework for separating large-scale retired batteries is developed. Specifically, the model is trained in the cloud and screens retired batteries in the edge plane. The experimental results show that the data transmission time of the cloud-edge collaborative framework is reduced by about 3 orders of magnitude than the cloud computing plane. Besides, the method achieves a 97 % classification accuracy rate. More importantly, the screening efficiency of the proposed approach is approximately 6 times higher than that of the conventional full charge-discharge testing methods. Additionally, the voltage consistency of the new module regrouped is significantly improved compared with the original module. The mean of the standard deviation is reduced by approximately 3 times.

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  • Gu, Xin & Li, Jinglun & Zhu, Yuhao & Wang, Yue & Mao, Ziheng & Shang, Yunlong, 2023. "A quick and intelligent screening method for large-scale retired batteries based on cloud-edge collaborative architecture," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027366
    DOI: 10.1016/j.energy.2023.129342
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    1. Ren, Lei & Zhou, Sheng & Ou, Xunmin, 2023. "The carbon reduction potential of hydrogen in the low carbon transition of the iron and steel industry: The case of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    2. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    3. Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).
    4. Richard Schmuch & Ralf Wagner & Gerhard Hörpel & Tobias Placke & Martin Winter, 2018. "Performance and cost of materials for lithium-based rechargeable automotive batteries," Nature Energy, Nature, vol. 3(4), pages 267-278, April.
    5. Weng, Caihao & Feng, Xuning & Sun, Jing & Peng, Huei, 2016. "State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking," Applied Energy, Elsevier, vol. 180(C), pages 360-368.
    6. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    7. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    8. 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).
    9. 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).
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