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| A Soft Clustering Method for the Large-Scale Retired Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy |
| Lai Xin1, Chen Quanwei1, Deng Cong1, Han Xuebing2, Zheng Yuejiu1 |
1. School of mechanical engineering University of Shanghai for Science and Technology Shanghai 200093 China;
2. School of vehicle and transportation Tsinghua University Beijing 100084 China |
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Abstract The sorting efficiency and accuracy of retired lithium-ion batteries (RLIBs) cannot be obtained at the same time, which seriously restricts the economy and safety of echelon utilization of large-scale RLIBs. To address these issues, a soft clustering method for the large-scale RLIBs based on Electrochemical Impedance Spectroscopy (EIS) is proposed in this study. First, the EIS test and distribution of relaxation times (DRT) analysis are conducted on RLIBs, and then a correlation model between battery capacity and DRT is established using the BP neural network, which is used for the rapid estimation of large-scale battery capacity. Second, six dimensional criteria such as battery capacity, ohmic internal resistance, and DRT characteristics are constructed. On this basis, a soft clustering method based on Gaussian mixture model is proposed. In this method, the important electrochemical characteristics in the battery is considered, and the soft clustering of RLIBs is implemented, which greatly improves the accuracy and flexibility of clustering results. Finally, the clustering results are verified by calculating the contour coefficients and performing HPPC experiments. Experimental results show that the time to obtain battery capacity is shortened from 3 hours in standard capacity test to 10 minutes, and the capacity prediction error is controlled within 4%. The proposed soft clustering method can improve the flexibility of battery regrouped and ensure the satisfactory consistency of regrouped batteries.
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Received: 26 August 2021
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2022, Vol. 37 
