Citation

Naha A, Khandelwal A, Agarwal S, Tagade P, Hariharan KS, Kaushik A, Yadu A, Kolake SM, Han S, Oh B. Sci. Rep. 2020; 10(1): e1301.

Affiliation

Advanced Battery Lab, Mobile Communication Business Division, SAMSUNG ELECTRONICS Co., Gyeonggi-do, 16677, Korea.

Copyright

(Copyright © 2020, Nature Publishing Group)

DOI

10.1038/s41598-020-58021-7

PMID

31992751

Abstract

With the proliferation of Li-ion batteries in smart phones, safety is the main concern and an on-line detection of battery faults is much wanting. Internal short circuit is a very critical issue that is often ascribed to be a cause of many accidents involving Li-ion batteries. A novel method that can detect the Internal short circuit in real time based on an advanced machine leaning approach, is proposed. Based on an equivalent electric circuit model, a set of features encompassing the physics of Li-ion cell with short circuit fault are identified and extracted from each charge-discharge cycle. The training feature set is generated with and without an external short-circuit resistance across the battery terminals. To emulate a real user scenario, internal short is induced by mechanical abuse. The testing feature set is generated from the battery charge-discharge data before and after the abuse. A random forest classifier is trained with the training feature set. The fault detection accuracy for the testing dataset is found to be more than 97%. The proposed algorithm does not interfere with the normal usage of the device, and the trained model can be implemented in any device for online fault detection.

Language: en