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Updated: Jul 10, 2026
08:42
In Situ Gas Analysis and Fire Characterization of Lithium-Ion Cells During Thermal Runaway Using an Environmental Chamber
Published on: March 31, 2023
Discovering Early-Stage Gas Generation Kinetics Enables Thermal Runaway Early Warning in Lithium-Ion Batteries
Jiabo Zhang1,2, Qianzhen Guo1, Shuaiqi Liu1
1Key Laboratory for Power Machinery and Engineering of the Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.
Summary
Early warning of lithium-ion battery thermal runaway is improved by detecting early gas generation during a chemical activation stage before significant heat accumulation. This new method enables earlier detection of battery failure.
Area of Science:
- Battery technology
- Electrochemistry
- Chemical engineering
Background:
- Conventional thermal runaway (TR) early warning methods for lithium-ion batteries are often triggered after irreversible failure, limiting their effectiveness.
- Existing indicators for TR typically emerge only after substantial heat accumulation (HA), necessitating improved predictive capabilities.
Purpose of the Study:
- To develop a novel framework for gas-based early warning of lithium-ion battery TR by identifying the earliest chemical processes preceding HA.
- To uncover and characterize a previously unrecognized chemical activation (CA) stage that occurs before HA.
Main Methods:
- Utilizing operando gas measurements to detect trace gas accumulation during the CA stage.
- Developing a physics-informed gas generation kinetics network (GGKNet) that integrates chemical reaction kinetics with gas adsorption-desorption, electrolyte evaporation, and lithium-involved reactions.
- Quantitatively capturing species-resolved gas generation and reconstructing pathway-resolved kinetics across different states of charge and TR stages.
Main Results:
- Identification of a distinct CA stage initiating at 60°C, characterized by coupled electrolyte-electrode reactions and continuous trace gas generation without measurable temperature rise.
- GGKNet successfully elucidates underlying mechanisms, quantitatively capturing gas generation and kinetics during CA and HA.
- Validation under TR scenarios shows GGKNet provides superior prediction of gas onset timing, temporal evolution, and spatial distribution.
Conclusions:
- The developed framework enables early warning of lithium-ion battery TR by detecting subtle gas changes during the CA stage.
- GGKNet translates kinetic-level insights into practical guidance for advanced battery safety systems.
- This approach significantly advances the capability for predicting and mitigating battery failures through early gas detection.
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