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

doi:10.3901/JME.2022.22.056

Abstract

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

CLC Number:

TM912

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.

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