(Invited) Quantitative Measurement of the Safety Performance of Li-Ion Batteries
Bibliographic record
Abstract
Safety is one of the essential requirements that should be taken into account and prioritized in the course of the development phases of lithium secondary batteries for any commercial applications. Even though different safety test standards exist to simulate the electric, mechanical, and thermal abuse conditions, there are only limited methods that can be used to quantify the safety performance of the cell. This study aims to compare the quantitative measurement techniques of safety evaluation and identify the methods to define the design parameters influencing the eventual safety performance of the full cells. The nail penetration, hot box, ARC (Accelerated Rate Calorimeter) and overcharging test are performed using the pouch cells (1-3 Ah) with different cell designs and aging conditions. It is found that hot box and nail penetration results are well correlated with the OCV (open circuit voltage) of the cells, suggesting that the OCV threshold can be used as a new quantitative indicator of the safety performance. Using this indicator, the cells having different P/E (power-to-energy ratio) designs and different electrolyte formulations are compared to identify the conditions to promote the safety performance of the cells. Figure 1
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.009 | 0.004 |
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".