Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network

Battery-related faults have become the most intractable problem hindering the further prosperity of fields like electric vehicle and grid energy storage. This paper is devoted to constructing a novel diagnostic framework for the faults in series battery packs, resorting to signal imaging and convolutional neural network (CNN) techniques. First, the voltage synchronicity between adjacent cells in a pack is quantified using the recursive correlation coefficient which can percept system anomalies sensitively. Then, reliant on the Gramian Angular Field (GAF) and Markov Transition Field (MTF) transformations, the correlation coefficient series is converted into pseudo images, the textures of which are full of informative details regarding system state. Finally, CNN models are employed to analyze the images for fault symptoms, thereby detecting fault occurrence, inferring fault type and evaluating fault grade. To obtain realistic dataset, different types and severities of faults are physically triggered on a li-ion battery pack. Experimental verification results indicate that the proposed framework can give accurate and reliable judgements on fault specifics, with the accuracy rates of fault type isolating and severity grading as 99.63% and 63.6% on GAF images, and as 99.75% and 58.7% on MTF images, respectively.

Automated summary

This paper proposes a novel diagnostic framework for faults in series battery packs using signal imaging and convolutional neural network techniques. It quantifies the voltage synchronicity between adjacent cells with high sensitivity to detect system anomalies. By converting the correlation coefficient series into pseudo images using GAF and MTF transformations, informative details about system state are extracted. CNN models are then used to analyze the images and accurately detect fault occurrence, infer fault type, and evaluate fault grade. Experimental results show high accuracy rates for fault type isolation and severity grading using the proposed framework.