Thermal and Mechanical Safety Assessment of Type 21700 Lithium-Ion Batteries with NMC, NCA and LFP Cathodes–Investigation of Cell Abuse by Means of Accelerating Rate Calorimetry (ARC)
Bibliographic record
Abstract
In this experimental investigation, we studied the safety and thermal runaway behavior of commercial lithium-ion batteries of type 21700. The different cathode materials NMC, NCA and LFP were compared, as well as high power and high energy cells. After characterization of all relevant components of the batteries to assure comparability, two abuse methods were applied: thermal abuse by the heat-wait-seek test and mechanical abuse by nail penetration, both in an accelerating rate calorimeter. Several critical temperatures and temperature rates, as well as exothermal data, were determined. Furthermore, the grade of destruction, mass loss and, for the thermal abuse scenario, activation energy and enthalpy, were calculated for critical points. It was found that NMC cells reacted first, but NCA cells went into thermal runaway a little earlier than NMC cells. LFP cells reacted, as expected, more slowly and at significantly higher temperatures, making the cell chemistry considerably safer. For mechanical abuse, no thermal runaway was observed for LFP cells, as well as at state of charge (SOC) zero for the other chemistries tested. For thermal abuse, at SOC 0 and SOC 30 for LFP cells and at SOC 0 for the other cell chemistries, no thermal runaway occurred until 350 °C. In this study, the experimental data are provided for further simulation approaches and system safety design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".