Data-Driven Diagnosis of Multiple Faults in Series Battery Packs Based on Cross-Cell Voltage Correlation and Feature Principal Components

This article develops an efficient fault diagnostic scheme for battery packs using a novel sensor topology and signal processing procedure. Cross-cell voltages are measured to capture electrical abnormalities, and recursive correlation coefficients between adjacent voltages are calculated to embody system state. Then discrete wavelet packet transform is applied on the correlation sequences to extract diverse characteristic indexes, wherein the most representative components are refined as fault features by principal component analysis. Afterward, resorting to multiclass relevance vector machine, sparse classification models are constructed to cognize fault patterns, and accordingly, fault types and grades are evaluated. Common faults, including external and internal short-circuit, thermal abuse, and loose connection, are physically triggered on a series pack to acquire realistic data set. Experimental verifications under different conditions and algorithmic configurations suggest that the proposed diagnosis scheme can give accurate and reliable assessments on different fault specifics, with a fault isolation success rate of 84% and a fault severity grading success rate of 90%.