Numerical Analysis of Gas Diffusion Characteristics During Thermal runaway in Lithium-ion battery module
Bibliographic record
Abstract
Lithium-ion batteries are widely used as secondary batteries due to their high energy density and low self-discharge.However, the lithium-ion batteries have the risk factor, such as explosions and toxic gas emissions caused by thermal runaway.Thermal runaway is a phenomenon where the temperature of the battery rapidly increases due to external factors, which leads to chemical reactions inside the battery, and the generation and ejection of various gases such as H2, CO, CO2, and VOCs to the outside.[1,2] Analyzing the vented gas flow from the battery is essential for designing fire safety devices, such as early detection systems for battery fires.The CO2 gas is known most advantageous for early detection of thermal runaway [3].However, most previous studies on gas flow analysis have been performed in single cells, while lithium-ion batteries are typically used as battery modules.[4,5] Therefore, this study was used to analyse the gas diffusion inside the module where thermal runaway of the battery occurred.This study performed numerical analysis using the commercial program Ansys fluent 19.1.The standard k-ε model is used for gas diffusion in the battery module and the species transport model to simulate the gas generated from the battery.The analysis was unsteady, with a time step of 0.01 s, and set for an analysis time of 100 s.The results show that at the beginning of gas ejection, the gas diffuses rapidly to adjacent cells where thermal runaway occurs.The gas velocity distribution before 7 seconds after thermal runaway describes the gradual diffusion of the gas into the module.However, the amount of gas initially ejected is small, and the CO2 mass fraction distribution before 10 seconds is low.After 8 seconds, there is a region where the gas diffusion rate suddenly increases.This is because of the flow that hits the module wall due to the vortex, and the flow moves into the center of the module due to the vortex.After 10 seconds, the released CO2 gas gradually accumulates at the end of the module and diffuses throughout the interior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".