Evaluation of detection criteria for thermal runaway experiments on commercial cells for electric vehicles
Bibliographic record
Abstract
With electric vehicles being more and more present on the roads, the possibility of battery fires attracts much attention. Such fires can start as thermal runaway events in a single cell and propagate to neighboring cells. Early detection of thermal runaways is crucial for passenger egress from the vehicle in case of fire. For Regulatory tests, international standards and regulations are available providing criteria based on cell voltage or temperature combined with the temperature rate dT/dt for the detection of thermal runway events. All documents use combinations of several criteria for the identification of thermal runaway events. However, there are differences in the definitions and combinations of these criteria. Based on international guidance documents, we specify thermal runaway detection criteria and apply these to data from thermal runaway tests on commercial single cells for electric vehicles. The runaway events were triggered by nail penetration or by heater on various cell formats. We compare the accuracy of combined criteria and thresholds with regard to their ability for distinguishing between thermal events that led to thermal runaway and those that did not. For our tests, the criteria based on cell temperature and its rate are more reliable than the combination of temperature rate and cell voltage.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".