Development of compact mechanism for lithium-ion battery venting gas fires using Cantera ordinary differential equation neural network algorithm

• Developed physically interpretable network to optimize chemical mechanism. • Created a compact mechanism for lithium-ion venting gas combustion. • Demonstrated high accuracy in species with the compact mechanism. Lithium-ion battery fires pose significant challenges to the development of electric vehicles and energy storage systems due to their potential hazards and complex combustion behavior. To address these issues, this work develops the Cantera Ordinary Differential Equation Neural Network (CODENN) algorithm, which combines the computational power of neural ordinary differential equations with Cantera's advanced chemical kinetics modeling. This integration allows for the optimization of a wide range of chemical reactions, improving both the precision and versatility of reaction mechanism development. Using CODENN, a compact mechanism (COM) was developed by optimizing the Arrhenius parameters of the RED mechanism, which had been overly reduced from the detailed CRECK2003 mechanism (114 species, 1999 reactions). CRECK2003 was chosen for its proven accuracy in predicting the combustion properties of LIB venting gases. The resulting COM mechanism, with 30 species and 213 reactions, achieves a high fidelity of 94.8 % in predicting ignition delay times across equivalence ratios from 0.3 to 2.5, demonstrating the reliability and robustness of CODENN algorithm. Further analysis of speciation data and CO net production rates shows that the COM mechanism closely aligns with the species evolution of the ORI mechanism during autoignition, while exhibiting notably more intense CO production and consumption than the ORI mechanism. Path flux analysis indicates that, despite having shorter reaction chains than the ORI mechanism, the COM mechanism preserves the fundamental physical logic of fuel consumption (CH₄) leading to H₂O formation while introducing additional pathways for the generation and consumption of H and OH radicals. Sensitivity analysis across diverse equivalence ratios and temperatures consistently identifies reaction R5 (H + O₂ ≤> O + OH) as the most temperature-sensitive reaction, underscoring its critical role in reaction kinetics of LIB venting gases.

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Publication Details

Journal
Applications in Energy and Combustion Science
Published
2025-02-20
DOI
https://doi.org/10.1016/j.jaecs.2025.100326
Citations
2
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
1.37
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article

Development of compact mechanism for lithium-ion battery venting gas fires using Cantera ordinary differential equation neural network algorithm

Huangwei Zhang, Li Lü, Mengjie Li, Hao Hu
2 citations
Applications in Energy and Combustion Science
Advanced Battery Technologies Research
1.37
article

Development of compact mechanism for lithium-ion battery venting gas fires using Cantera ordinary differential equation neural network algorithm

Huangwei Zhang, Li Lü, Mengjie Li, Hao Hu
article en
2 citations

Abstract

• Developed physically interpretable network to optimize chemical mechanism. • Created a compact mechanism for lithium-ion venting gas combustion. • Demonstrated high accuracy in species with the compact mechanism. Lithium-ion battery fires pose significant challenges to the development of electric vehicles and energy storage systems due to their potential hazards and complex combustion behavior. To address these issues, this work develops the Cantera Ordinary Differential Equation Neural Network (CODENN) algorithm, which combines the computational power of neural ordinary differential equations with Cantera's advanced chemical kinetics modeling. This integration allows for the optimization of a wide range of chemical reactions, improving both the precision and versatility of reaction mechanism development. Using CODENN, a compact mechanism (COM) was developed by optimizing the Arrhenius parameters of the RED mechanism, which had been overly reduced from the detailed CRECK2003 mechanism (114 species, 1999 reactions). CRECK2003 was chosen for its proven accuracy in predicting the combustion properties of LIB venting gases. The resulting COM mechanism, with 30 species and 213 reactions, achieves a high fidelity of 94.8 % in predicting ignition delay times across equivalence ratios from 0.3 to 2.5, demonstrating the reliability and robustness of CODENN algorithm. Further analysis of speciation data and CO net production rates shows that the COM mechanism closely aligns with the species evolution of the ORI mechanism during autoignition, while exhibiting notably more intense CO production and consumption than the ORI mechanism. Path flux analysis indicates that, despite having shorter reaction chains than the ORI mechanism, the COM mechanism preserves the fundamental physical logic of fuel consumption (CH₄) leading to H₂O formation while introducing additional pathways for the generation and consumption of H and OH radicals. Sensitivity analysis across diverse equivalence ratios and temperatures consistently identifies reaction R5 (H + O₂ ≤> O + OH) as the most temperature-sensitive reaction, underscoring its critical role in reaction kinetics of LIB venting gases.

Applications in Energy and Combustion ScienceVol. 22
National University of Singapore (SG)
Affordable and clean energy
Openalex Percentile: Top 21%
Advanced Battery Technologies Research
1.37
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