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Development of a Robust Early-Stage Thermal Runaway Detection Model for Lithium-ion Batteries

Published

Author(s)

Wai Cheong Tam, Jian Chen, Wei Tang, Qi Tong, Hongqiang Fang, Anthony D. Putorti Jr.

Abstract

This paper presents the development of a fast-responding and accurate detection model for early-stage thermal runaway of a lithium-ion battery utilizing acoustics and a deep learning paradigm. A series of single-cell lithium-ion battery tests is conducted. Different state-of-charge conditions and battery orientations are considered. Acoustic data are extracted from video recordings. Using data augmentation, 1 330 acoustic samples of early-stage thermal runaway are obtained. To facilitate the development of a detection model that can be used in real-life settings, 1 128 samples of acoustic data, including various human activities, are also used. Utilizing 10-s acoustic data as the input and a convolutional neural network model structure as the backbone, the detection model has an overall accuracy of 93.9 % with a precision and recall score of 91.6 % and 97.7 %, respectively. Parametric studies are carried out to evaluate the robustness of the proposed model structure and the effectiveness of the data augmentation methods. In addition, the model performance against two entire tests is assessed using leave-one test-out cross-validation. It is hoped that the proposed work can help to develop a robust detection device that can provide early warning of thermal runaways and allow users to have extra time to mitigate the potential extreme fire hazards and/or to safely evacuate.
Proceedings Title
13th Asia-Oceania Symposium on Fire Science and Technology
Conference Dates
October 21-25, 2024
Conference Location
Daegu, KR

Keywords

thermal runaway prevention, acoustic signals, machine learning, safety valve breakage (SVB), data augmentation, detection

Citation

Tam, W. , Chen, J. , Tang, W. , Tong, Q. , FANG, H. and Putorti Jr., A. (2024), Development of a Robust Early-Stage Thermal Runaway Detection Model for Lithium-ion Batteries, 13th Asia-Oceania Symposium on Fire Science and Technology, Daegu, KR, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958183 (Accessed January 2, 2025)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created October 22, 2024, Updated December 31, 2024