基于振动信号的锂离子电池故障诊断方法

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Abstract In order to realize the fault diagnosis of overcharge and external short circuit of lithium-ion battery, a feature extraction method of lithium-ion battery vibration signal based on improved variable mode decomposition (VMD)-multiscale entropy (MSE) is proposed. Through the improved VMD decomposition of vibration signal, the multiscale entropy value of the natural mode component is obtained, and the vibration characteristics of lithium-ion battery under different working conditions are extracted. Finally, based on this feature, K-means clustering is carried out to complete the fault identification of overcharge and external short circuit fault. The experimental results show that this method can effectively extract the vibration signal features of lithium-ion battery, and correctly identify the overcharge and external short circuit faults of lithium-ion battery, and the accuracy is better.

Key words： lithium-ion battery fault diagnosis vibration signal feature extraction overcharge fault external short circuit fault

Received: 15 March 2021

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	Articles by authors
	YIN Laibin
	XU Honghua
	PENG Xiaohan
	XIA Weidong
	MA Hongzhong

Cite this article:

YIN Laibin,XU Honghua,PENG Xiaohan等. Fault diagnosis method of lithium-ion battery based on vibration signal[J]. Electrical Engineering, 2021, 22(10): 71-75.

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http://dqjs.cesmedia.cn/EN/Y2021/V22/I10/71

[1] 安富强, 赵洪量, 程志, 等. 纯电动车用锂离子电池发展现状与研究进展[J]. 工程科学学报, 2019, 41(1): 22-42.
[2] 白桦, 王正用, 李晨, 等. 面向电网侧、新能源侧及用户侧的储能容量配置方法研究[J]. 电气技术, 2021, 22(1): 8-13.
[3] 张圣祺, 袁蓓, 季振东, 等. 基于分布式控制原理的电池储能系统二次调频控制[J]. 电工技术学报, 2019, 34(增刊2): 637-645.
[4] 朱晓庆, 王震坡, WANG Hsin, 等. 锂离子动力电池热失控与安全管理研究综述[J]. 机械工程学报, 2020, 56(14): 91-118.
[5] ZHAO Xingwang, XU Liangfei, LI Jianqiu, et al.Faults diagnosis for PEM fuel cell system based on multi-sensor signals and principle component analysis method[J]. International Journal of Hydrogen Energy, 2017, 42(29): 18524-18531.
[6] 朱小平, 张涛. 基于自适应理论的锂离子电池SOC估计[J]. 电气技术, 2013, 14(7): 47-50.
[7] 常晨, 刘兰荣, 卢美林, 等. 并联电抗器等效模型的振动噪声特性试验研究[J]. 电气技术, 2019, 20(2): 37-41, 47.
[8] 马速良, 武建文, 袁洋, 等. 多振动信息下的高压断路器机械故障随机森林融合诊断方法[J]. 电工技术学报, 2020, 35(增刊2): 421-431.
[9] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE Transactions on Signal Pro- cessing, 2014, 62(3): 531-544.
[10] 彭晓晗, 马宏忠, 许洪华, 等. 基于振动的储能电池异常工况预警新方法[J/OL]. 电测与仪表: 1-6 [2021-02-22]. http://kns.cnki.net/kcms/detail/23.1202.TH. 20200519.1527.002.html.
[11] 马宏忠, 吴元熙, 劳文洁, 等. 基于MRSVD和时频灰度图的储能电池过充特征识别[J]. 电源技术, 2020, 44(9): 1351-1355.
[12] 毕凤荣, 汤代杰, 张立鹏, 等. 基于VMD与KFCM的柴油机故障诊断算法[J]. 振动.测试与诊断, 2020, 40(5): 853-858, 1018.
[13] 黄衍, 林建辉, 刘泽潮, 等. 基于自适应VMD的高速列车轴箱轴承故障诊断[J]. 振动与冲击, 2021, 40(3): 240-245.
[14] 高艳丰, 朱永利, 闫红艳, 等. 基于VMD和TEO的高压输电线路雷击故障测距研究[J]. 电工技术学报, 2016, 31(1): 24-33.
[15] 谢丽蓉, 杨欢, 李进卫, 等. 基于GA-ENN特征选择和参数优化的双馈风电机组轴承故障诊断[J]. 太阳能学报, 2021, 42(1): 149-156.
[16] 李军锋. 基于GA-VMD谐波检测及高阶奇异谱的电能质量分析方法研究[D]. 秦皇岛: 燕山大学, 2019.
[17] 贾亚飞, 朱永利, 王刘旺, 等. 基于VMD和多尺度熵的变压器内绝缘局部放电信号特征提取及分类[J].电工技术学报, 2016, 31(19): 208-217.
[18] 罗朋, 吴健威, 于跃, 等. 基于k均值聚类算法的无均衡自适应锂电储能研究[J]. 电气技术, 2020, 21(5): 33-40, 54.