一种基于电化学阻抗谱的大规模退役锂离子电池的软聚类方法

doi:10.19595/j.cnki.1000-6753.tces.211343

摘要
图/表
参考文献
相关文章 (9)

全文: PDF (6389 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要退役锂离子电池的分选目前存在效率与精度不可兼得的问题,严重制约大规模退役锂电池梯次利用的经济性与安全性。该文针对以上问题,提出一种基于电化学阻抗谱（EIS）的退役锂离子电池软聚类方法。首先,对退役锂离子电池进行EIS测试和弛豫时间（DRT）分析,利用BP神经网络建立电池容量与DRT关联模型,并用于大规模电池容量的快速估计。然后,构建电池容量、欧姆内阻与DRT特征等六维度判据,在此基础上提出一种基于高斯混合模型的电池软聚类方法。该方法在考虑电池内部重要电化学特征的基础上实现了退役锂离子电池的软聚类,大大提高了聚类结果的准确性与灵活性。最后,通过计算轮廓系数和进行混合脉冲功率特性（HPPC）实验对聚类结果进行验证。实验结果表明,获取电池容量的时间由标准容量测试的3h缩短到10min,容量预测误差控制在4%以内;所提出的软聚类分类方法能提高电池重组的灵活性,并能保证重组电池具有很好的一致性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	来鑫
	陈权威
	邓聪
	韩雪冰
	郑岳久

关键词 ：容量估计, 退役锂离子电池, 软聚类, 电化学阻抗谱, 弛豫时间

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.

Key words： Capacity estimation retired lithium-ion batteries soft clustering electrochemical impedance spectroscopy distribution of relaxation times

收稿日期: 2021-08-26

PACS:

TM912

基金资助:国家自然科学基金项目（51977131,51877138）、上海市自然科学基金项目（19ZR1435800）和汽车安全与节能国家重点实验室项目（KF2020）资助

通讯作者: 来鑫男,1983年生,副教授,博士生导师,研究方向为锂电池的全生命周期管理、优化与控制。E-mail：laixin@usst.edu.cn

作者简介: 陈权威男,1995年生,博士研究生,研究方向为锂电池的全生命周期管理与控制。E-mail：chenqw_2021@163.com

引用本文:

来鑫, 陈权威, 邓聪, 韩雪冰, 郑岳久. 一种基于电化学阻抗谱的大规模退役锂离子电池的软聚类方法[J]. 电工技术学报, 2022, 37(23): 6054-6064. Lai Xin, Chen Quanwei, Deng Cong, Han Xuebing, Zheng Yuejiu. A Soft Clustering Method for the Large-Scale Retired Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy. Transactions of China Electrotechnical Society, 2022, 37(23): 6054-6064.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.211343 https://dgjsxb.ces-transaction.com/CN/Y2022/V37/I23/6054

[1] 宫明辉, 乌江, 焦朝勇. 基于模糊自适应扩展卡尔曼滤波器的锂电池SOC估算方法[J]. 电工技术学报, 2020, 35(18): 3972-3978.
Gong Minghui, Wu Jiang, Jiao Chaoyong.SOC estimation method of lithium battery based on fuzzy adaptive extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(18): 3972-3978.
[2] 张振宇, 汪光森, 聂世雄, 等. 脉冲大倍率放电条件下磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2019, 34(8): 1769-1779.
Zhang Zhenyu, Wang Guangsen, Nie Shixiong, et al.State of charge estimation of LiFePO₄ battery under the condition of high rate pulsed discharge[J]. Transactions of China Electrotechnical Society, 2019, 34(8): 1769-1779.
[3] 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.
[4] Casals L C, García B A, Canal C.Second life batteries lifespan: rest of useful life and environmental analysis[J]. Journal of Environmental Management, 2019, 232: 354-363.
[5] Xu Zhicheng, Wang Jun, Lund P D, et al.A novel clustering algorithm for grouping and cascade utilization of retired Li-ion batteries[J]. Journal of Energy Storage, 2020, 29: 101303.
[6] Lai Xin, Huang Yunfeng, Gu Huanghui, et al.Turning waste into wealth: a systematic review on echelon utilization and material recycling of retired lithium-ion batteries[J]. Energy Storage Materials, 2021, 40: 96-123.
[7] 孙冬, 许爽. 梯次利用锂电池健康状态预测[J]. 电工技术学报, 2018, 33(9): 2121-2129.
Sun Dong, Xu Shuang.State of health prediction of second-use lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2018, 33(9): 2121-2129.
[8] Lai Xin, Huang Yunfeng, Deng Cong, et al.Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: a critical review[J]. Renewable and Sustainable Energy Reviews, 2021, 146: 111162.
[9] 李晓宇, 徐佳宁, 胡泽徽, 等. 磷酸铁锂电池梯次利用健康特征参数提取方法[J]. 电工技术学报, 2018, 33(1): 9-16.
Li Xiaoyu, Xu Jianing, Hu Zehui, et al.The health parameter estimation method for LiFePO₄ battery echelon use[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 9-16.
[10] 徐佳宁, 倪裕隆, 朱春波. 基于改进支持向量回归的锂电池剩余寿命预测[J]. 电工技术学报, 2021, 36(17): 3693-3704.
Xu Jianing, Ni Yulong, Zhu Chunbo.Remaining useful life prediction for lithium-ion batteries based on improved support vector regression[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3693-3704.
[11] 徐余丰, 严加斌, 何建明, 等. 退役动力锂电池在光储微电网的集成与应用[J]. 储能科学与技术, 2021, 10(1): 349-354.
Xu Yufeng, Yan Jiabin, He Jianming, et al.Integration and application of retried LIBs in photovoltaic and energy storage micro grid[J]. Energy Storage Science and Technology, 2021, 10(1): 349-354.
[12] 聂江霖, 杨江朋, 蔡春健, 等. 基于多端口变压器的串联锂电池均压电路[J]. 电工技术学报, 2021, 36(20): 4274-4284.
Nie Jianglin, Yang Jiangpeng, Cai Chunjian, et al.Voltage equalizing circuit of series lithium battery based on multi-port transformer[J]. Transactions of China Electrotechnical Society, 2021, 36(20): 4274-4284.
[13] Lai Xin, Qiao Dongdong, Zheng Yuejiu, et al.A rapid screening and regrouping approach based on neural networks for large-scale retired lithium-ion cells in second-use applications[J]. Journal of Cleaner Production, 2019, 213: 776-791.
[14] Zhou Long, He Long, Zheng Yuejiu, et al.Massive battery pack data compression and reconstruction using a frequency division model in battery management systems[J]. Journal of Energy Storage, 2020, 28: 101252.
[15] He Xiangming, Zhang Gan, Feng Xuning, et al.A facile consistency screening approach to select cells with better performance consistency for commercial 18650 lithium ion cells[J]. International Journal of Electrochemical Science, 2017,12(11): 10239-10258.
[16] 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.
[17] Zhang Xiaohu, Zhang Xiong, Sun Xianzhong, et al.Electrochemical impedance spectroscopy study of lithium-ion capacitors: modeling and capacity fading mechanism[J]. Journal of Power Sources, 2021, 488: 229454.
[18] 范文杰, 徐广昊, 于泊宁, 等. 基于电化学阻抗谱的锂离子电池内部温度在线估计方法研究[J]. 中国电机工程学报, 2021, 41(9): 3283-3293.
Fan Wenjie, Xu Guanghao, Yu Boning, et al.On-line estimation method for internal temperature of lithium-ion battery based on electrochemical impedance spectroscopy[J]. Proceedings of the CSEE, 2021, 41(9): 3283-3293.
[19] 任东生, 冯旭宁, 韩雪冰, 等. 锂离子电池全生命周期安全性演变研究进展[J]. 储能科学与技术, 2018, 7(6): 957-966.
Ren Dongsheng, Feng Xuning, Han Xuebing, et al.Recent progress on evolution of safety performance of lithium-ion battery during aging process[J]. Energy Storage Science and Technology, 2018, 7(6): 957-966.
[20] Zhou Xing, Huang Jun, Pan Zhengqiang, et al.Impedance characterization of lithium-ion batteries aging under high-temperature cycling: importance of electrolyte-phase diffusion[J]. Journal of Power Sources, 2019, 426: 216-222.
[21] 王佳, 黄秋安, 李伟恒, 等. 电化学阻抗谱弛豫时间分布基础[J]. 电化学, 2020, 26(5): 607-627.
Wang Jia, Huang Qiuan, Li Weiheng, et al.Fundamentals of distribution of relaxation times for electrochemical impedance spectroscopy[J]. Journal of Electrochemistry, 2020, 26(5): 607-627.
[22] Zhang Li, Wang Fulin, Sun Ting, et al.A constrained optimization method based on BP neural network[J]. Neural Computing and Applications, 2018, 29: 413-421.
[23] 魏孟, 李嘉波, 叶敏, 等. 基于高斯混合回归的锂离子电池SOC估计[J]. 储能科学与技术, 2020, 9(3): 958-963.
Wei Meng, Li Jiabo, Ye Min, et al.SOC estimation of Li-ion battery based on gaussian mixture regression[J]. Energy Storage Science and Technology, 2020, 9(3): 958-963.
[24] 张成, 白建波, 兰康, 等. 基于数据挖掘和遗传小波神经网络的光伏电站发电量预测[J]. 太阳能学报, 2021, 42(3): 375-382.
Zhang Cheng, Bai Jianbo, Lan Kang, et al.Photovoltaic power generation prediction based on data mining and genetic wavelet neural network[J]. Acta Energiae Solaris Sinica, 2021, 42(3): 375-382.