基于机械振动信号的锂离子电池组连接故障诊断

doi:10.3901/JME.2022.22.056

机械工程学报 ›› 2022, Vol. 58 ›› Issue (22): 56-68.doi: 10.3901/JME.2022.22.056

• 特邀专栏:车载电化学能源系统 • 上一篇下一篇

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基于机械振动信号的锂离子电池组连接故障诊断

申东旭¹, 吕超¹, 葛亚明², 张刚¹, 杨大智¹, 王立欣³

1. 哈尔滨工业大学电气工程及自动化学院哈尔滨 150001;
2. 哈尔滨工业大学(深圳)实验与创新教育中心深圳 518071;
3. 哈尔滨工业大学(深圳)机电工程与自动化学院深圳 518071

收稿日期:2022-05-10 修回日期:2022-08-20 出版日期:2022-11-20 发布日期:2023-02-07
通讯作者: 吕超(通信作者),男,1978年出生,博士,教授,博士研究生导师。主要研究方向为动力/储能电池管理新理论与新技术。E-mail:lu_chao@hit.edu.cn
作者简介:申东旭,男,1995年出生,博士研究生。主要研究方向为锂离子电池健康管理与故障诊断。E-mail:13522903163@163.com
基金资助:
广东省重点研发资助项目-乘用车三元动力电池系统主动安全防控技术资助项目(2020B090919004)

Connection Fault Diagnosis of Lithium-ion Battery Pack Based on Mechanical Vibration Signals

SHEN Dong-xu¹, Lü Chao¹, GE Ya-ming², ZHANG Gang¹, YANG Da-zhi¹, WANG Li-xin³

1. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001;
2. Education Center of Experiments and Innovations, Harbin Institute of Technology, Shenzhen 518071;
3. School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518071

Received:2022-05-10 Revised:2022-08-20 Online:2022-11-20 Published:2023-02-07

摘要/Abstract

摘要： 为满足高电压大容量的实际应用场景和需求,锂离子电池组往往由成百上千的电池单体通过螺栓、焊接等方式串并联组成。电池组单体间的连接故障会导致接触电阻升高和连接处异常发热,严重影响电池组的性能和安全。提出一种基于机械振动信号的锂离子电池组连接故障诊断方法。利用压电陶瓷传感器实现电压信号和振动信号的相互转换,在每种故障模式下采集振动信号;基于稀疏测度指标和熵测度方法在频域和时域提取故障特征以描述锂离子电池组在不同连接故障模式下的故障特性;利用最大相关最小冗余算法降低高维特征空间的冗余度,选择出最重要的特征;在此基础上,建立基于差分进化算法优化的支持向量机诊断模型。结果表明,该方法诊断准确度为0.963,可以准确检测到锂离子电池组的连接故障并明确故障发生的位置。

关键词: 锂离子电池组, 连接故障诊断, 压电传感技术, 特征提取

Abstract: In order to meet the practical application scenarios and needs of high voltage and large capacity, lithium-ion battery packs are often composed of hundreds or thousands of battery cells connected in series and parallel through bolts and welding. The connection failure between the cells of the battery pack can lead to increased contact resistance and abnormal heating at the connection, which can seriously affect the performance and safety of the battery pack. A connection fault diagnosis method for lithium-ion battery packs based on mechanical vibration signals is proposed. The piezoelectric ceramic sensor is used to realize the mutual conversion of the voltage signal and the vibration signal, and the vibration signal is collected in each fault mode. Based on sparse measure metrics and entropy measure method, fault features are extracted in frequency and time domains to describe the fault characteristics of lithium-ion battery packs under different connection fault modes. The maximum relevance minimum redundancy algorithm is used to reduce the redundancy of high-dimensional feature space and select the most important features; On this basis, the diagnosis model of support vector machine optimized by differential evolution algorithm is established. The results show that the diagnostic accuracy of this method is 0.963, which means that this method can accurately detect the connection fault of lithium-ion battery pack and determine the location of the fault.

Key words: lithium-ion battery pack, connection fault diagnosis, piezoelectric ceramic sensor, feature extraction

中图分类号:

TM912

申东旭, 吕超, 葛亚明, 张刚, 杨大智, 王立欣. 基于机械振动信号的锂离子电池组连接故障诊断[J]. 机械工程学报, 2022, 58(22): 56-68.

SHEN Dong-xu, Lü Chao, GE Ya-ming, ZHANG Gang, YANG Da-zhi, WANG Li-xin. Connection Fault Diagnosis of Lithium-ion Battery Pack Based on Mechanical Vibration Signals[J]. Journal of Mechanical Engineering, 2022, 58(22): 56-68.

参考文献

[1] HU Xiaosong,DENG Zhongwei,LIN Xianke,et al.Research directions for next-generation battery management solutions in automotive applications[J].Renewable and Sustainable Energy Reviews,2021,152:111695.
[2] 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.
[3] 熊瑞,马骕骁,杨瑞鑫,等.动力电池外部短路故障热-力影响与分析[J].机械工程学报,2019,55(2):115-125.XIONG Rui, MA Suxiao, YANG Ruixin, et al.Thermo-mechanical influence and analysis of external short circuit faults in lithium-ion battery[J]. Journal of Mechanical Engineering,2019,55(2):115-125.
[4] 张抗抗,徐梁飞,华剑锋,等.基于多目标优化的纯电动车动力系统参数匹配方法[J].汽车工程,2015,37(7):757-765.ZHANG Kangkang,XU Liangfei,HUA Jianfeng,et al.A parameter matching method for the powertrain of battery electric vehicle based on multi-objective optimization[J]. Automotive Engineering,2015,37(7):757-765.
[5] PAN Yue,FENG Xuning,ZHANG Mingxuan,et al.Internal short circuit detection for lithium-ion battery pack with parallel-series hybrid connections[J]. Journal of Cleaner Production,2020,255:120277.
[6] 来鑫,秦超,郑岳久,等.基于恒流充电曲线电压特征点的锂离子电池自适应容量估计方法[J].汽车工程,2019,41(1):1-6.LAI Xin,QIN Chao,ZHENG Yuejiu,et al. An adaptive capacity estimation scheme for lithium-ion battery based on voltage characteristic points in constant-current charging curve[J]. Automotive Engineering,2019,41(1):1-6.
[7] 戴海峰,孙泽昌,魏学哲.利用双卡尔曼滤波算法估计电动汽车用锂离子动力电池的内部状态[J].机械工程学报,2009,45(6):95-101.DAI Haifeng,SUN Zechang,WEI Xuezhe. Estimation of internal states of power lithium-ion batteries used on electric vehicles by dual extended Kalman filter[J].Journal of Mechanical Engineering,2009,45(6):95-101.
[8] ZHENG Yuejiu, LU Yifan, GAO Wenkai, et al.Micro-short-circuit cell fault identification method for lithium-ion battery packs based on the mutual information[J]. IEEE Transactions on Industrial Electronics,2020,68(5):4373-4381.
[9] TRAN M K,FOWLER M. A review of lithium-ion battery fault diagnostic algorithms:Current progress and future challenges[J]. Algorithms,2020,13(3):62.
[10] 蔡雪,张彩萍,张琳静,等.基于等效电路模型的锂离子电池峰值功率估计的对比研究[J].机械工程学报,2021,57(14):64-76.CAI Xue,ZHANG Caiping,ZHANG Linjing,et al.Comparative study on state of power estimation of lithium ion battery based on equivalent circuit model[J]. Journal of Mechanical Engineering,2021,57(14):64-76.
[11] 陈泽宇,熊瑞,孙逢春.电动汽车电池安全事故分析与研究现状[J].机械工程学报,2019,55(24):93-104,116.CHEN Zeyu,XIONG Rui,SUN Fengchun. Research status and analysis for battery safety accidents in electric vehicles[J]. Journal of Mechanical Engineering,2019,55(24):93-104,116.
[12] DAI Haifeng,JIANG Bo,HU Xiaosong,et al. Advanced battery management strategies for a sustainable energy future:multilayer design concepts and research trends[J].Renewable and Sustainable Energy Reviews,2020,138:110480.
[13] 孙振宇,王震坡,刘鹏,等.新能源汽车动力电池系统故障诊断研究综述[J].机械工程学报,2021,57(14):87-104.SUN Zhenyu,WANG Zhenpo,LIU Peng,et al. Overview of fault diagnosis in new energy vehicle power battery system[J]. Journal of Mechanical Engineering,2021,57(14):87-104.
[14] SIDHU A,IZADIAN A,ANWAR S. Adaptive nonlinear model-based fault diagnosis of li-ion batteries[J]. IEEE Transactions on Industrial Electronics,2015,62(2):1002-1011.
[15] 徐佳宁,梁栋滨,魏国,等.串联电池组接触电阻故障诊断分析[J].电工技术学报,2017,32(18):106-112.XU Jianing,LIANG Dongbin,WEI Guo,et al. Series battery pack's contact resistance fault diagnosis analysis[J]. Transactions of China Electrotechnical Society,2017,32(18):106-112.
[16] 韩冰海,王东梅,李世云.一种基于直流内阻的电池组连接可靠性的检测方法[J].电源技术,2017,41(7):981-983.HAN Binghai,WANG Dongmei,LI Shiyun. Research on battery connection reliability based on DC internal resistance[J]. Chinese Journal of Power Sources,2017,41(7):981-983.
[17] 杨洋,杨林,李冬冬,等.电动汽车电池组连接松脱故障诊断[J].汽车技术,2019(5):43-48.YANG Yang,YANG Lin,LI Dongdong,et al. A loose connection fault diagnosis for battery pack of electric vehicle[J]. Automobile Technology,2019(5):43-48.
[18] MA Mina,WANG Yu,DUAN Qiangling,et al. Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis[J]. Energy,2018,164:745-756.
[19] MA Mina,DUAN Qiangling,ZHAO Chunpeng,et al.Faulty characteristics and identification of increased connecting and internal resistance in parallel-connected lithium-ion battery pack for electric vehicles[J]. IEEE Transactions on Vehicular Technology,2020,69(10):10797-10808.
[20] YAO Lei,WANG Zhenpo,MA Jun,et al. Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles[J]. Journal of Power Sources,2015,293:548-561.
[21] 中华人民共和国工业与信息化部. JB/T 6040-2011工程机械螺栓拧紧力矩的检验方法[S].北京:中国标准出版社,2011.Ministry of Industry and Information Technology in China.JB/T 6040-2011 Construction machinery Test methods of tightening torque for bolts[S]. Beijing:Standards Press of China,2011.
[22] WANG Dong,PENG Zhike,XI Lifeng,et al. The sum of weighted normalized square envelope:A unified framework for kurtosis,negative entropy,Gini index and smoothness index for machine health monitoring[J].Mechanical Systems and Signal Processing,2020,140:106725.
[23] LI Yuwen, FUJITA H, LI Jianqing, et al. Tensor approximate entropy:An entropy measure for sleep scoring[J]. Knowledge-Based Systems, 2022, 245:108503.
[24] SAMPAI D L,NICOLETTI R. Detection of cracks in shafts with the approximated entropy algorithm[J].Mechanical Systems and Signal Processing,2016,72-73:286-302.
[25] BREIMAN L. Random forests[J]. Machine Learning,2001,45(1):5-32.
[26] PETERSON L E. K-nearest neighbor[J]. Scholarpedia,2009,4(2):1883.
[27] PRADHAN A. Support vector machine-A survey[J].International Journal of Emerging Technology and Advanced Engineering,2012,2(8):82-85.
[28] 党月懋,张雪纯,徐楚奕,等.基于温度变化率曲线的锂离子电池健康状态评估算法[J].电气工程学报,2022,17(3):58-65.DANG Yuemao,ZHANG Xuechun,XU Chuyi,et al.Lithium-ion battery state of health assessment algorithm based on DT curve[J]. Journal of Electrical Engineering,2022,17(3):58-65.
[29] 杨瑞鑫,熊瑞,孙逢春.锂离子动力电池外部短路测试平台开发与试验分析[J].电气工程学报,2021,16(1):103-118.YANG Ruixin, XIONG Rui, SUN Fengchun.Experimental platform development and characteristics analysis of external short circuit in lithium-ion batteries[J].Journal of Electrical Engineering,2021,16(1):103-118.

基于机械振动信号的锂离子电池组连接故障诊断

Connection Fault Diagnosis of Lithium-ion Battery Pack Based on Mechanical Vibration Signals

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