Influence of ambient temperature, discharge C‐rate, and convective heat transfer coefficient on thermal behaviour of lithium‐ion battery pack: A numerical study
Bibliographic record
Abstract
Abstract Lithium‐ion batteries play a crucial role in reducing carbon emissions and promoting the use of electric vehicles. There are numerous input variables influencing the thermal profile of lithium‐ion batteries. Therefore, a precise assessment of the relative contributions of various factors is essential for optimizing thermal management and control processes. In this study, we tested a lithium‐ion battery pack composed of five 14.6 Ah prismatic cells connected in series under different discharge rates (2C, 3C, 4C, and 5C), ambient temperatures (30, 35, 40, and 45°C), and convective heat transfer coefficients (5, 10, 20, and 40 ). Results showed that the ambient temperature with a contribution of 58.01% had a strong influence on the maximum battery pack temperature. Furthermore, the influences of discharge C‐rate and convective heat transfer coefficient on the maximum battery pack temperature were identical. Moreover, it was found that the homogeneousness of the battery pack was very sensitive to the discharge C‐rate, contributing 71.07% to the increase in temperature difference. To ensure battery pack temperature and temperature uniformity at the same time, moderate ambient temperatures, low discharge C‐rates, and high convective heat transfer coefficients can be preferred. Consequently, the statistically obtained results in this study may contribute towards performance optimization and improved thermal safety of lithium‐ion battery packs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".