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Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
Published on: March 7, 2022
Research on Thermal Runaway Monitoring Methods for Lithium-Ion Batteries Based on Continuous Acoustic Emission
Bingxi Liu1,2, Fumin Li3, Xiaoyang Bi3
1The Key Laboratory of Fire Protection Technology for Industry and Public Building, Ministry of Emergency Management, Tianjin 300381, China.
Sensors (Basel, Switzerland)
|July 15, 2026
Summary
Acoustic emission technology offers ultra-early detection of internal lithium-ion battery (LIB) thermal runaway (TR) reactions. This novel method overcomes limitations of traditional monitoring for enhanced energy storage safety.
Area of Science:
- Materials Science
- Electrical Engineering
- Safety Engineering
Background:
- Lithium-ion batteries (LIBs) are prevalent but pose safety risks due to thermal runaway (TR).
- Existing TR detection methods (temperature, strain) have inherent delays, missing the critical incubation phase.
- Internal sensors for TR detection are impractical due to harsh battery environments and high costs.
Purpose of the Study:
- To introduce acoustic emission (AE) technology for real-time, external detection of internal TR reactions in LIBs.
- To address the limitations of current methods in detecting the ultra-early incubation stage of TR.
- To develop a proactive risk-management strategy for energy storage safety.
Main Methods:
- An experimental platform was designed to induce TR via overcharging in LIBs.
- Multi-source acoustic emission (AE) and temperature signals were acquired concurrently during TR events.
- Time-frequency analysis and a two-dimensional method were employed for AE signal processing and feature recognition, coupled with a convolutional neural network for phase segmentation.
Main Results:
- Continuous AE signals were successfully captured from the initiation of overcharging until battery venting.
- Anomalous waveform features indicative of early TR were identified using time-frequency analysis.
- The AE-based method, enhanced by a convolutional neural network, achieved high-accuracy phase segmentation for TR events, outperforming temperature-based methods.
Conclusions:
- Acoustic emission (AE) technology provides an effective and precise means for ultra-early detection of internal TR in LIBs.
- AE monitoring offers a significant advancement over traditional methods, enabling proactive risk assessment.
- This technology holds substantial potential for enhancing safety and dynamic risk management in energy storage applications.
