NIST - National Institute of Standards and Technology
03/15/2026 | Press release | Distributed by Public on 03/16/2026 03:13
On the Use of Machine Learning for the Development of an Acoustic-based Detection System for Early-Stage Thermal Runaway
Published
March 15, 2026
Author(s)
Wai Cheong Tam, Md. Ismail Siddiqi Emon, Jian Chen, Hongqiang Fang, Jun Deng, Anthony Putorti
Abstract
The paper presents the development of a multi-class classification model for the detection of early-stage thermal runaway events for button-top single-cell lithium-ion batteries. A signal gate mechanism is introduced to extract relevant acoustic data. A data alignment technique is applied to enhance the model training. A multi-layer two-dimensional convolutional neural network is utilized to learn the important features to differentiate non-thermal runaway events and thermal runaway events. Results show that the proposed model can detect the thermal runaway events with an overall accuracy, precision, and recall of 96.7 %, 78.8 %, and 91.8 %, respectively. Sensitivity studies are conducted and the results indicate that the data alignment and data augmentation techniques help to enhance the model performance significantly. The finding from this paper hopes to contribute to the development of a practical and accurate early-stage thermal runaway detection model that can provide early warning to users to avoid battery fires.
Citation
Process Safety and Environmental Protection
Pub Type
Journals
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Citation
Tam, W. , Emon, M. , Chen, J. , FANG, H. , Deng, J. and Putorti, A. (2026), On the Use of Machine Learning for the Development of an Acoustic-based Detection System for Early-Stage Thermal Runaway, Process Safety and Environmental Protection, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960166 (Accessed March 16, 2026)
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NIST - National Institute of Standards and Technology published this content on March 15, 2026, and is solely responsible for the information contained herein. Distributed via Public Technologies (PUBT), unedited and unaltered, on March 16, 2026 at 09:13 UTC. If you believe the information included in the content is inaccurate or outdated and requires editing or removal, please contact us at [email protected]