Soft clustering of retired lithium-ion batteries for the secondary utilization using Gaussian mixture model based on electrochemical impedance spectroscopy

Xin LAI*, Cong DENG, Xiaopeng TANG, Furong GAO, Xuebing HAN, Yuejiu ZHENG*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

36 Citations (Scopus)

Abstract

Rapid sorting and reasonable regrouping of retired lithium-ion batteries (LIBs) are directly related to the economy and safety of the second-life utilization. However, the efficiency and accuracy of sorting for the retired LIBs needs to be further improved, and the regrouping method is still in the exploratory stage. In this study, a soft clustering method based on the Gaussian mixture model (GMM) using electrochemical impedance spectroscopy (EIS) is proposed to address these issues. In this method, the multi-dimensional clustering criteria are extracted from EIS, and the capacity is quickly estimated based on the EIS using a neural network. Furthermore, the ageing factors of six criteria are constructed to realize the soft clustering of retired cells corresponding to three ageing modes. The simulation results show that it only takes about 10 min to obtain the capacity of each cell, and the error is within 4%. Moreover, the clustering probability of each cell under different ageing modes is obtained using GMM, which is useful for flexible grouping of cells. Finally, the proposed methods are evaluated by experiments, and results show that the consistency of the regrouped cells using the proposed soft-clustering method is nearly doubled than that of the random regrouped cells.

Original languageEnglish
Article number130786
JournalJournal of Cleaner Production
Volume339
Early online date2 Feb 2022
DOIs
Publication statusPublished - 10 Mar 2022
Externally publishedYes

Bibliographical note

Funding Information:
This work is supported by the National Natural Science Foundation of China (Grant No. 51977131 and No. 51877138), the Natural Science Foundation of Shanghai (Grant No. 19ZR1435800), the State Key Laboratory of Automotive Safety and Energy under Project No. KF2020, and Shanghai Science and Technology Development Fund (Grant No. 19QA1406200).

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • Electrochemical impedance spectroscopy
  • Fast capacity estimation
  • Gaussian mixture model
  • Retired lithium-ion batteries
  • Soft clustering

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