Multi-Fault Diagnosis of Electric Vehicle Power Battery Based on Double Fault Window Location and Fast Classification

Abstract

As energy supply units, lithium-ion batteries have been widely used in the electric vehicle industry. However, the safety of lithium-ion batteries remains a significant factor limiting their development. To achieve rapid fault diagnosis of lithium-ion batteries, this paper presents a comprehensive fault diagnosis process. Firstly, an interleaved voltage sensor topology structure is utilized to acquire battery voltage data. An improved complete ensemble empirical mode decomposition with adaptive noise method is introduced to process data. Then, the reconstructed voltage data sequence is used to eliminate the influence of noise. A fault location is performed using dichotomy correlation coefficient and time window correlation coefficient. Afterwards, principal component analysis is used to select the principal components with high contribution rate as classification features. The gray wolf optimization algorithm is used to find the parameters of the least squares support vector machine, constructing an optimal classifier for fault classification. A fault experiment platform is established to realize the physical triggering of faults such as external short circuit, internal circuit, and connection of experimental battery packs. Finally, the accuracy and reliability of the method are verified by the results of fault localization and fault type determination.