Several installations equipped with lithium-ion batteries may require additional precautions. While lithium-ion batteries offer good performance relative to other rechargeable batteries, their state of health should be monitored. Faulty lithium-ion batteries may be vulnerable to thermal runaway or explosion. Early detection of those vulnerabilities can be done accurately by using an effective charging-anomaly detection method. In this paper, a binary support vector machine classification method was used to detect faulty lithium-ion batteries that are being recharged with constant voltage. The support vector machine algorithm was trained on battery data acquired after the recharging was finished. The battery data consisted of temperature, voltage, and varying recharging current measured inside the lithium-ion battery. Estimation losses, sensitivity, and receiver operating characteristic curves were computed and presented after training and testing the algorithm. Class labels and classifier’s generalization performance information were also displayed. An estimation loss of 7% was found at the end of this research.
Açıcı et al. (Thu,) studied this question.