Analysis of ejecta ignition and velocity from 18650 Li-ion battery cells during thermal runaway
Bibliographic record
Abstract
Lithium-ion batteries are increasingly deployed across a range of energy storage applications, heightening the need to understand failure mechanisms such as thermal runaway. This study presents a new experimental platform focusing on optical accessibility to quantify the transient ejection and ignition dynamics from a single 18650-format cell with an NMC-811 cathode. High-speed optical diagnostics, including luminosity imaging, spectral emission, photodetection, and sound measurements, were employed to characterise the spatiotemporal evolution of ejected fragments and flaming ignition. Light and sound data are temporally correlated, demonstrating that ignition is followed by flame propagation that occurs rapidly and intermittently, with significant temporal variability despite consistent thermal runaway initiation temperatures. A gaseous ignition mode is identified where solid fragments ejected from the battery coated with metallic lithium ignite pyrophorically, acting as an ignition kernel for the flammable gaseous effluent. Atomic emission of neutral lithium (Li I) at 611 nm and 671 nm is detected during ignition and flaming ignition and is hypothesised to form via lithium plating or thermal decomposition of LiC 6 during thermal runaway. Atomic emission of sodium and potassium is also found, all of which is superimposed on the grey body distribution from luminous ejecta. In addition, the high-speed images are cross-correlated to provide time-resolved velocity fields and their statistical distributions of ejected particles during the entire thermal runaway event. Axial velocities possess a log-normal distribution, with peak velocities as large as 35 m s −1 . This work sets the platform for subsequent studies employing high-speed optical diagnostics to deliver a quantitative database to enable modelling of Li-ion battery thermal runaway and to understand flame-induced heating of adjacent cells in a battery module.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".