Faults Diagnosis for Large-Scale Battery Packs via Texture Analysis on Spatial–Temporal Images Converted From Electrical Behaviors

Battery failures have become the most intractable obstacles undermining the market confidence in applications like electric vehicle and power grid energy storage. This article aims to fashion a generic diagnosis scheme against the faults in large-scale battery systems. First, a voltmeter array-based anomaly perception mechanism against the electrical behaviors of battery packs is developed. Then, system information on spatial arrangement and temporal dynamics is organically fused and drawn as a kind of pseudo 2-D images (P2Is). Afterward, by analyzing the resultant P2Is with the 2-D variational mode decomposition (2-D-VMD) and gray level co-occurrence matrix (GLCM), some statistical quantities concerning multi-scale texture features, extracted and refined by the principal component analysis (PCA), are found to have strong indicative associations with battery fault type and fault grade. Finally, relying on the multi-class relevance vector machine (M-RVM), feature evidences are synthesized to detect fault occurrence and give judgments on fault specifics of type and severity. Experimental verifications on a Li-ion battery (LiB) pack with 180 cells suggest that the proposed scheme behaves well in fault type isolating, with an accuracy rate of 97.6%, and in fault severity grading, with an accuracy rate of 84.67%.