一种基于几何特征变换与迁移的锂离子电池电化学阻抗谱曲线重构方法

doi:10.3901/JME.2023.22.140

机械工程学报 ›› 2023, Vol. 59 ›› Issue (22): 140-149.doi: 10.3901/JME.2023.22.140

• 特邀专栏：动力电池安全应用技术 • 上一篇下一篇

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一种基于几何特征变换与迁移的锂离子电池电化学阻抗谱曲线重构方法

来鑫¹, 马云杰¹, 郑岳久¹, 韩雪冰²

1. 上海理工大学机械工程学院上海 200093;
2. 清华大学车辆与运载学院北京 100084

收稿日期:2023-03-03 修回日期:2023-08-05 出版日期:2023-11-20 发布日期:2024-02-19
通讯作者: 郑岳久(通信作者)，男，1986年出生，博士，教授，博士研究生导师。主要研究方向为电池全寿命周期管理。E-mail：yuejiu.zheng@usst.edu.cn
作者简介:来鑫，男，1983年出生，博士，副教授，博士研究生导师。主要研究方向为下一代电池管理系统与先进控制、退役锂离子电池的梯次利用、动力电池全生命周期评价与可持续发展。E-mail：laixin@usst.edu.cn
基金资助:
国家自然科学基金资助项目(51977131，52277223)。

Reconstruction Method of Electrochemical Impedance Spectrum Curve of Lithium-ion Batteries Based on Geometric Feature Transformation and Migration

LAI Xin¹, MA Yunjie¹, ZHENG Yuejiu¹, HAN Xuebing²

1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093;
2. School of Vehicle and Transportation, Tsinghua University, Beijing 100084

Received:2023-03-03 Revised:2023-08-05 Online:2023-11-20 Published:2024-02-19

摘要/Abstract

摘要： 利用电化学阻抗谱(Electrochemical impedance spectroscopy， EIS)技术对锂离子电池进行无损检测是电池健康度评价及分选的有效手段。然而， EIS 测试时需要将电池的荷电状态(State of charge， SOC)调整到一致，大大降低了测试效率。针对上述问题，提出一种基于几何特征变换与迁移的锂离子电池 EIS 曲线重构方法。首先，将 EIS 曲线图形化为三个圆弧和一段直线的组合几何体，并定义这些几何体的特征参数；其次，通过小批量电池 EIS 测试数据建立几何特征参数与 SOC 之间的线性关系；然后，将上述关系迁移到大批量电池的 EIS 曲线重构，实现将任意 SOC 下测试的 EIS 曲线重构到同一目标 SOC 下；最后，利用试验验证所提出方法的有效性。试验结果表明，重构 EIS 曲线与实测 EIS 曲线之间的方均根误差及平均绝对百分比误差分别保持在 1.2 mΩ 与 3.5%以内，测试时间相比传统方法缩短了至少 98%。所提出方法简单易实现，在保证 EIS 曲线重构精度下可大幅度减小测量时间。

关键词: 锂离子电池, 电化学阻抗谱, 剩余电荷状态, 几何特征, 梯次利用, 电动汽车

Abstract: Non-destructive testing of lithium-ion batteries(LIBs) by electrochemical impedance spectroscopy(EIS) is an effective method for battery health evaluation and selection. However, when EIS is tested, the charge state(SOC) of batteries needs to be adjusted to a uniform level, which greatly reduces the testing efficiency. Aiming at these problems, a method of EIS curve reconstruction for LIBs based on geometric feature transformation is presented in this study. Firstly, the EIS curve is graphed as a combination of three circular arcs and a straight line, and the characteristic parameters of these geometries are defined. Subsequently, the linear relationship between geometric feature parameters and SOC is established through small-batch EIS test data. Then, the above relationships are transferred to the EIS curve reconstruction of a large number of batteries, and the EIS curve measured under any SOC can be reconstructed to the target SOC. Finally, the proposed method is verified by experiments. The results show that the root-mean-square-error and the average-absolute-percentage-error between the reconstructed EIS curve and the measured EIS curve are controlledby 1.2 mΩ and 3.5%, respectively, and the test time is reduced by at least 98% compared with the traditional methods. The proposed method is simple and easy to implement, and can significantly reduce the measurement time while ensuring the EIS accuracy.

Key words: lithium-ion battery, electrochemical impedance spectroscopy, state of charge, geometric feature, cascade utilization, electric vehicle

中图分类号:

TM912

来鑫, 马云杰, 郑岳久, 韩雪冰. 一种基于几何特征变换与迁移的锂离子电池电化学阻抗谱曲线重构方法[J]. 机械工程学报, 2023, 59(22): 140-149.

LAI Xin, MA Yunjie, ZHENG Yuejiu, HAN Xuebing. Reconstruction Method of Electrochemical Impedance Spectrum Curve of Lithium-ion Batteries Based on Geometric Feature Transformation and Migration[J]. Journal of Mechanical Engineering, 2023, 59(22): 140-149.

参考文献

[1] ZHAO W, ZHOU X, WANG C, et al. Energy analysis and optimization design of vehicle electro-hydraulic compound steering system[J]. Applied Energy, 2019, 255:113713.
[2] AXSEN J, CAIRNS J, DUSYK N, et al. What drives the Pioneers? Applying lifestyle theory to early electric vehicle buyers in Canada[J]. Energy Research & Social Science, 2018, 44:17-30.
[3] 王珍. 新能源汽车产销量连续7 年居世界第一[N]. 中国纪检监察报, 2022-10-19(6). WANG Zhen. The production and sales of new energy vehicles ranked first in the world for seven consecutive years[N]. China Discipline Inspection Newspaper, 2022-10-19(6).
[4] LAI X, CHEN Q, TANG X, et al. Critical review of life cycle assessment of lithium-ion batteries for electric vehicles:A lifespan perspective[J]. eTransportation, 2022, 12:100169
[5] 欧阳明高. 我国节能与新能源汽车发展战略与对策[J]. 汽车工程, 2006, 28(4):317-321. OUYANG Minggao. Development strategies and countermeasures for energy conservation and new energy vehicles in China[J]. Automotive Engineering, 2006, 28(4):317-321.
[6] BUKHARI S, MAQSOOD J, BAIG M Q, et al. Comparison of characteristics -Lead acid, nickel based, lead crystal and lithium based batteries[C]//Uksim-amss International Conference on Modelling & Simulation. IEEE, 2015:978-1-4799-8713.
[7] ZHONG X, LIU W, HAN J, et al. Pretreatment for the recovery of spent lithium ion batteries:Theoretical and practical aspects[J]. Journal of Cleaner Production, 2020, 263:121439.
[8] ZENG X, LI J, LIU L. Solving spent lithium-ion battery problems in China:Opportunities and challenges[J]. Renewable and Sustainable Energy Reviews, 2015, 52:1759-1767.
[9] 刘若桐,李建林,吕喆,等. 退役动力电池应用潜力分析[J]. 电气技术, 2021, 22(8):1-9. LIU Ruotong, LI Jianlin, LÜ Zhe, et al. Analysis of application potential of retired power batteries[J]. Electrical Engineering, 2021, 22(8):1-9.
[10] HUA Y, ZHOU S, HUANG Y, et al. Sustainable value chain of retired lithium-ion batteries for electric vehicles[J]. Journal of Power Sources, 2020, 478:228753.
[11] WILSON N, MEIKLEJOHN E, OVERTON B, et al. A physical allocation method for comparative life cycle assessment:A case study of repurposing Australian electric vehicle batteries[J]. Resources, Conservation and Recycling, 2021, 174:105759.
[12] 蔡铭,陈维杰,许俊斌,等. 退役LiFePO_4电池梯次利用分选方法[J]. 电源技术, 2019, 43(5):781-784. CAI Ming, CHEN Weijie, XU Junbin, et al. Retired LiFePO_4 sorting method of battery cascade utilization[J]. Chinese Journal of Power Sources, 2019, 43(5):781-784.
[13] 殷娟娟,王伟贤,袁小溪,等. 退役锂电池快速评价及分选方法研究[J]. 重庆理工大学学报(自然科学), 2020, 34(2):15-23. YIN Juanjuan, WANG Weixian, YUAN Xiaoxi, et al. Research on rapid evaluation and sorting methods of retired lithium batteries[J]. Journal of Chongqing University of Technology(Natural Science), 2020, 34(2):15-23.
[14] 乔冬冬. 大规模退役锂离子电池的快速筛选与分类方法研究[D]. 上海:上海理工大学, 2019. QIAO Dongdong. Research on rapid screening and classification methods of large-scale decommissioned lithium-ion batteries[D]. Shanghai:University of Shanghai for Science and Technology, 2019.
[15] 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. Fast sorting technology of retired lithium battery modules based on fast charging curve[J]. Power System Technology, 2020, 44(5):1664-1673.
[16] BERNARDES A M, ESPINOSA D C R, TENóRIO J A S. Recycling of batteries:A review of current processes and technologies[J]. Journal of Power Sources, 2004, 130(1):291-298.
[17] 王放放,冯祥明,赵光金,等. 电化学阻抗谱识别不同化学体系退役动力锂离子电池[J]. 储能科学与技术, 2023, 12(2):609. WANG Fangfang, FENG Xiangming, ZHAO Guangjin, et al. Electrochemical impedance spectroscopy identification of retired power lithium-ion batteries in different chemical systems[J]. Energy Storage Science and Technology, 2023, 12(2):609.
[18] 张彩萍,姜久春,张维戈,等. 梯次利用锂离子电池电化学阻抗模型及特性参数分析[J]. 电力系统自动化, 2013, 37(1):54-58. ZHANG Caiping, JIANG Jiuchun, ZHANG Weige, et al. Electrochemical impedance model and characteristic parameter analysis of lithium ion battery in cascade utilization[J]. Automation of Electric Power Systems, 2013, 37(1):54-58.
[19] 骆凡,黄海宏,王海欣. 基于短时脉冲放电与电化学阻抗谱的退役动力电池快速分选与重组方法[J]. 仪器仪表学报, 2022, 43(1):229-238. LUO Fan, HUANG Haihong, WANG Haixin. A rapid separation and reorganization method for retired power batteries based on short-time pulse discharge and electrochemical impedance spectroscopy[J]. Chinese Journal of Scientific Instrument, 2022, 43(1):229-238.
[20] 戴海峰,王冬晨,姜波. 基于电化学阻抗谱的电池荷电状态估计[J]. 同济大学学报(自然科学版), 2019, 47(增刊 1):95-98. DAI Haifeng, WANG Dongchen, JIANG Bo. Estimation of battery state of charge based on electrochemical impedance spectroscopy[J]. Journal of Tongji University(Natural Science), 2019, 47(Suppl.1):95-98.
[21] 来鑫,陈权威,邓聪,等. 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, et al. A soft clustering method for large-scale retired lithium ion batteries based on electrochem ical impedance spectroscopy[J]. Transactions of China Electrotechnical Society, 2022, 37(23):6054-6064.
[22] 张宇鑫,武建华,郑林锋,等. 基于数字孪生的锂离子电池管理系统设计分析[J]. 电气工程学报, 2022, 17(4):103-112. ZHANG Yuxin, WU Jianhua, ZHENG Linfeng, et al. Design and analysis of lithium-ion battery management system based on digital twin[J]. Journal of Electrical Engineering, 2022, 17(4):103-112.
[23] 方德宇,楚潇,刘涛,等. 基于数据-模型驱动的锂离子电池健康状态估计[J]. 电气工程学报, 2022, 17(4):20-31. FANG Deyu, CHU Xiao, LIU Tao, et al. Research on health assessment method of lithium-ion battery based on data-model hybrid drive[J]. Journal of Electrical Engineering, 2022, 17(4):20-31.
[24] HEAVISIDE O. On the transformation of optical wave-surfaces by homogeneous strain[J]. Proceedings of the Royal Society of London, 1894, 55:30-43.

一种基于几何特征变换与迁移的锂离子电池电化学阻抗谱曲线重构方法

Reconstruction Method of Electrochemical Impedance Spectrum Curve of Lithium-ion Batteries Based on Geometric Feature Transformation and Migration

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