Thermal Model of Cylindrical and Prismatic Lithium-Ion Cells
Bibliographic record
Abstract
Oven exposure testing is a standard benchmark that Li-ion cells must pass in order to be approved for sale by regulating bodies. In order to test the safety of new cell designs or electrode materials, manufacturers must make small test batches of cells. This can be both costly and time consuming. Using reaction kinetics that have been developed for electrode materials with electrolyte exposed to high temperature, and thermal properties of cells from the literature, a predictive model for oven exposure testing has been developed. The model predictions are compared to oven exposure test results for E-One/Moli Energy, Canada, 18650 /graphite cells and shown to be in good agreement. The model can predict the response of new cell sizes and electrode materials to oven exposure testing without actually producing any cells. This is illustrated with a number of examples: ( i ) increasing the specific surface area of the graphite electrode; ( ii ) using or other cathode substitutes instead of ; ( iii ) varying the diameter of cylindrical cells; and ( iv ) varying the thickness of prismatic cells. © 2001 The Electrochemical Society. All rights reserved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".