基于改进DBSCAN的退役动力电池分选方法

doi:10.19595/j.cnki.1000-6753.tces.220187

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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.

Key words： Retired power battery echelon utilization sorting DBSCAN adaptive

Received: 10 February 2022

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TM912

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	Liu Zhengyu
	Guo Lekai
	Meng Hui
	Zhang Zheng
	Liu Xiang

Cite this article:

Liu Zhengyu,Guo Lekai,Meng Hui等. A Sorting Method of Retired Power Battery Based on Improved DBSCAN[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 3073-3083.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.220187 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I11/3073

[1] 李建林, 李雅欣, 吕超, 等. 退役动力电池梯次利用关键技术及现状分析[J]. 电力系统自动化, 2020, 44(13): 172-183.
Li Jianlin, Li Yaxin, Lü Chao, et al.Key technology and research status of cascaded utilization in decommissioned power battery[J]. Automation of Electric Power Systems, 2020, 44(13): 172-183.
[2] 赵伟, 闵婕, 李章溢, 等. 基于一致性模型的梯次利用锂离子电池组能量利用率估计方法[J]. 电工技术学报, 2021, 36(10): 2190-2198.
Zhao Wei, Min Jie, Li Zhangyi, et al.Energy utilization efficiency estimation method for second-use lithium-ion battery packs based on a battery consistency model[J]. Transactions of China Electrote-chnical Society, 2021, 36(10): 2190-2198.
[3] 华旸, 周思达, 何瑢, 等. 车用锂离子动力电池组均衡管理系统研究进展[J]. 机械工程学报, 2019, 55(20): 73-84.
Hua Yang, Zhou Sida, He Rong, et al.Review on lithium-ion battery equilibrium technology applied for EVs[J]. Journal of Mechanical Engineering, 2019, 55(20): 73-84.
[4] 刘璐, 牛萌, 郑伟杰, 等. 考虑内部参数不一致性的储能系统用锂电池组建模[J]. 电力系统自动化, 2021, 45(19): 15-23.
Liu Lu, Niu Meng, Zheng Weijie, et al.Modeling of lithium-ion battery packs for energy storage system considering inconsistency of internal battery parameters[J]. Automation of Electric Power Systems, 2021, 45(19): 15-23.
[5] 郑岳久, 李家琦, 朱志伟, 等. 基于快速充电曲线的退役锂电池模块快速分选技术[J]. 电网技术, 2020, 44(5): 1664-1673.
Zheng Yuejiu, Li Jiaqi, Zhu Zhiwei, et al.Rapid classification based on fast charging curves for reuse of retired lithium-ion battery modules[J]. Power System Technology, 2020, 44(5): 1664-1673.
[6] Zhang R, Zhou Y, Li R.A battery sorting scheme based on fuzzy C-mean clustering, taking advantage of the flatness of discharge voltage curve[J]. Automotive Engineering, 2017, 39(8): 864-869.
[7] Li Ran, Yao Jie, Zhou Yongqin.Study on sorting method of zinc silver battery based on multi-step FCM clustering algorithm[J]. IEICE Electronics Express, 2019, 16(7): 20190120.
[8] He Fengxian, Shen W X, Song Qiang, et al.Self-organising map based classification of LiFePO₄ cells for battery pack in EVs[J]. International Journal of Vehicle Design, 2015, 69(1/2/3/4): 151.
[9] 周治平, 王杰锋, 朱书伟, 等. 一种改进的自适应快速AF-DBSCAN聚类算法[J]. 智能系统学报, 2016, 11(1): 93-98.
Zhou Zhiping, Wang Jiefeng, Zhu Shuwei, et al.An improved adaptive and fast AF-DBSCAN clustering algorithm[J]. CAAI Transactions on Intelligent Systems, 2016, 11(1): 93-98.
[10] Kim J H, Choi J H, Yoo K H, et al.AA-DBSCAN: an approximate adaptive DBSCAN for finding clusters with varying densities[J]. The Journal of Supercomputing, 2019, 75(1): 142-169.
[11] Falahiazar Z, Bagheri A, Reshadi M.Determining the parameters of DBSCAN automatically using the multi-objective genetic algorithm[J]. Journal of Information Science and Engineering, 2021, 37: 157-183.
[12] 李阳, 马骊, 樊锁海. 基于动态近邻的DBSCAN算法[J]. 计算机工程与应用, 2016, 52(20): 80-85.
Li Yang, Ma Li, Fan Suohai.Improved DBSCAN clustering algorithm based on dynamic neighbor[J]. Computer Engineering and Applications, 2016, 52(20): 80-85.
[13] 王帅, 尹忠东, 郑重, 等. 电池模组一致性影响因素在放电电压曲线簇上的表征[J]. 电工技术学报, 2020, 35(8): 1836-1847.
Wang Shuai, Yin Zhongdong, Zheng Zhong, et al.Representation of influence factors for battery module consistency on discharge voltage curves[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1836-1847.
[14] Saha B, Goebel K.Battery data set[R]. NASA AMES prognostics data repository, CA: Moffett Field, 2007.
[15] Yun Liu, Sandoval J, Zhang Jian, et al.Lithium-ion battery packs formation with improved electrochemical performance for electric vehicles: experimental and clustering analysis[J]. Journal of Electrochemical Energy Conversion and Storage, 2019, 16(2): 021011.
[16] 戴海峰, 姜波, 魏学哲, 等. 基于充电曲线特征的锂离子电池容量估计[J]. 机械工程学报, 2019, 55(20): 52-59.
Dai Haifeng, Jiang Bo, Wei Xuezhe, et al.Capacity estimation of lithium-ion batteries based on charging curve features[J]. Journal of Mechanical Engineering, 2019, 55(20): 52-59.
[17] Lyu Chao, Song Yankong, Wang Lixin, et al.A new method for lithium-ion battery uniformity sorting based on internal criteria[J]. Journal of Energy Storage, 2019, 25: 100885.
[18] Xu You, Wu Jing, Xu Wei, et al.Performance matrix analysis method of power battery system based on multi-parameters’model[J]. Journal of Electrochemical Energy Conversion and Storage, 2021, 18(2): 020902.
[19] 潘海鸿, 张沫, 王惠民, 等. 基于多影响因素建立锂离子电池充电内阻的动态模型[J]. 电工技术学报, 2021, 36(10): 2199-2206.
Pan Haihong, Zhang Mo, Wang Huimin, et al.Establishing a dynamic model of lithium-ion battery charging internal resistance based on multiple factors[J]. Transactions of China Electrotechnical Society, 2021, 36(10): 2199-2206.
[20] 孙丙香, 任鹏博, 陈育哲, 等. 锂离子电池在不同区间下的衰退影响因素分析及任意区间的老化趋势预测[J]. 电工技术学报, 2021, 36(3): 666-674.
Sun Bingxiang, Ren Pengbo, Chen Yuzhe, et al.Analysis of influencing factors of degradation under different interval stress and prediction of aging trend in any interval for lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 666-674.
[21] 姜久春, 高洋, 张彩萍, 等. 电动汽车锂离子动力电池健康状态在线诊断方法[J]. 机械工程学报, 2019, 55(20): 60-72, 84.
Jiang Jiuchun, Gao Yang, Zhang Caiping, et al.Online diagnostic method for health status of lithium-ion battery in electric vehicle[J]. Journal of Mechanical Engineering, 2019, 55(20): 60-72, 84.
[22] Wang Qiuting, Qi Wei.Study on influence of sorting parameters to lithium-ion battery pack life-cycles based on cell consistency[J]. International Journal of Electric and Hybrid Vehicles, 2018, 10(3): 223.
[23] Tian Jiaqiang, Wang Yujie, Liu Chang, et al.Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles[J]. Energy, 2020, 194: 116944.
[24] 石琼林, 郭东旭, 杨耕, 等. 具有磷酸铁锂电池负极特征的SOC区间的确定方法[J]. 电工技术学报, 2020, 35(19): 4097-4105.
Shi Qionglin, Guo Dongxu, Yang Geng, et al.A method to determine characteristic region of negative electrode with state of charge for lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4097-4105.