Detection, classification and quantification of short circuits in batteries using a short fatigue metric

• Published in: Journal of energy storage Vol. 61; p. 106729
• Main Authors: Bharathraj, Sagar, Lee, MyeongJae, Adiga, Shashishekar P., Song, Taewon, Mayya, K. Subramanya, Kim, Jin-Ho
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2023
• Subjects: Battery safety; BMS algorithm; Early stage short detection; Hysteresis; Li-ion battery; Short circuit identification and classification; Short fatigue metric; Short circuit identification and classification; Battery safety; Li-ion battery; BMS algorithm; Early stage short detection; Short fatigue metric; Hysteresis
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2023.106729

A new metric to detect, classify and estimate the severity of short circuits in batteries is introduced in this work. State-of-the-art techniques mostly focus on the detection part and not much work is done on appropriately quantifying its severity. Barring accidental events, a majority of the short circuits have a long incubation period, where the short resistance continually decreases, to a point of thermal runaway. Thus, apart from detecting the short during its inception, a metric to track the severity, map it against a predetermined threshold to flag a potential catastrophe would be of great practical utility. A short fatigue metric (SFM) is proposed, based on the charge/discharge hysteresis, to classify short circuits into soft and hard. The SFM, which is more sensitive than other short-specific battery signatures, provides a fluid classification of short circuits, with continuous values, as a function of the short leakage current, ranging from 0 (no-fault cell) to 1 (hard cell), where 0.1 is defined as the soft-hard transition point. With emulated, persistent short circuit experiments on commercial batteries, the SFM is verified, to show that it is a useful metric to detect short especially in its early stages, classify and accurately estimate its severity. [Display omitted] •A holistic tool for detection, classification and quantification of short-circuits•Nascent stage short-circuit detection, which is chemistry or form factor independent•Short fatigue metric (SFM) based on charge-discharge hysteresis, specific to short•Experimental validation on commercial batteries shows >98 % accuracy.•Highly robust, easily implementable battery management system (BMS) algorithm