@inproceedings{6e31055e61e94d71b9ef68e74db6c8ea,
title = "Improved internal short circuit detection method for Lithium-Ion battery with self-diagnosis characteristic",
abstract = "Internal short circuit (ISC) has been proven to be responsible for the thermal runaway failure of lithium-ion battery (LIB). The accurate detection of the ISC failure at the early stage is critical to improve the safety of electric vehicles. In this paper, a ISC detection method with self-diagnostic feature is proposed according to the onboard measured load current and terminal voltage. The state of charge (SOC) is first estimated based on the extended Kalman filter (EKF). The ISC current of the cell is self-calibrated leveraging the EKF-estimated SOC and the measured load current. The estimated ISC current is further median filtered to reduce the stochastic error caused by the uncertainty of SOC estimation. Finally, the filtered ISC current are used to identify the ISC resistance online with the recursive least squares with variable forgetting factor (RLSVF) algorithm. Results suggest that the proposed method can identify the internal short circuit resistance online accurately with a high robustness to the noise disturbance.",
keywords = "extended Kalman filter, internal short circuit, lithium-ion battery, recursive least squares, state of charge",
author = "Jian Hu and Zhongbao Wei and Hongwen He",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 ; Conference date: 19-10-2020 Through 21-10-2020",
year = "2020",
month = oct,
day = "18",
doi = "10.1109/IECON43393.2020.9254885",
language = "English",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
pages = "3741--3746",
booktitle = "Proceedings - IECON 2020",
address = "United States",
}