Abstract：The rapid growth of electric vehicle sales has led to an increase in the number of decommissioned lithium power batteries. Testing and echelon utilization sorting for retired batteries can maximize the service life of batteries. Traditional battery echelon utilization sorting methods usually only consider external static parameters and neglect the difference between battery dynamic characteristics, so the sorting accuracy is limited. Recently, some sorting methods based on dynamic characteristic curves have been proposed, but most of them have problems such as complex feature extraction and poor optimization of algorithm parameters. This paper proposes a DBSCAN algorithm based on kernel density estimation (KDE-DBSCAN). The parameters of the clustering algorithm are adaptively determined by kernel density estimation, and the combination of static features and dynamic features is clustered to achieve battery sorting. Firstly, this study adopts sixth-order polynomial to adaptively fit the charge and discharge curve which is collected by the detetion system in sections. Secondly, extract the HVR (height of voltage rise) and DVF (depth of voltage falls) as the dynamic characteristics during charging and discharging, and the available capacity and open circuit voltage after charging as the static features. Thirdly, the extracted dynamic and static features of the battery are combined to form a four-dimensional feature vector and normalized. Finally, the feature sample points are clustered by DBSCAN clustering algorithm to finish the sorting of retired batteries after the parameters of the method are adaptively set by kernel density estimation. In this method, kernel density estimation solves the problem of DBSCAN parameter optimization, reduces human interference, and fully improves the accuracy and robustness of clustering. The sorting and grouping results of KDE-DBSCAN, DBSCAN and K-means clustering algorithms are compared and verified by charge discharge experiments. The results show that in the cell characteristics experiment, the mean value of battery voltage sorted by KDE-DBSCAN is larger and the variance is smaller compared with DBSCAN and K-means clustering algorithms. The average and variance of the polarization voltage of the battery pack based on KDE-DBSCAN sorting are significantly lower than those of the other two methods. The smaller mean value indicates that the overall polarization of the battery pack is smaller. The smaller variance indicates that the polarization fluctuation between single cells in the battery pack is smaller, and the battery pack is more inconsistent. In the battery module cycle life comparison experiment, from the 20th cycle to the 50th cycle, the decrease in discharge time of the KDE-DBSCAN sorted battery is significantly lower than that of batteries sorted by K-means clustering and DBSCAN clustering. After 50 cycles, the capacity of the former decreases by about 10.2% compared with the initial capacity. For the battery packs sorted by K-means and DBSCAN clustering, the numbers are 20.8% and 18.2% respectively. The following conclusions can be drawn from the experimental analysis: 1) The adaptive subsection fitting method can effectively solve the problem of low sampling frequency of battery detection system and improve the accuracy of feature extraction. 2) The combination of dynamic and static characteristics can better characterize the inconsistency of retired batteries. 3) Kernel density estimation is introduced to determine the parameters of DBSCAN algorithm, which reduces the instability of manual parameter adjustment and improves the accuracy and robustness of clustering.
刘征宇, 郭乐凯, 孟辉, 张政, 刘项. 基于改进DBSCAN的退役动力电池分选方法[J]. 电工技术学报, 2023, 38(11): 3073-3083.
Liu Zhengyu, Guo Lekai, Meng Hui, Zhang Zheng, Liu Xiang. A Sorting Method of Retired Power Battery Based on Improved DBSCAN. Transactions of China Electrotechnical Society, 2023, 38(11): 3073-3083.
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2023, Vol. 38 
