Study on Temperature Uniformity and Rising Rate of High-Power Lithium-Ion Battery Pack
Bibliographic record
Abstract
According to the operating characteristics of high-power battery pack in practical application, the effects of four factors, which include operating load, state of charge (SOC), ambient temperature and operating temperature, on the temperature uniformity and rising rate of battery pack under natural convection were studied based on Arbin test bench. The results indicate that: the operating load and SOC of battery pack doesn’t affect its temperature uniformity, which was deteriorated as the increasing of operating temperature of battery pack, or improved as the increasing of ambient temperature when the other factor is kept constant. The increment of operating load or ambient temperature could result in a higher temperature rising rate, while the increasing of SOC or operating temperature could lead to a lower temperature rising rate, when the other three factors are kept constant. When the operating temperature or temperature differences achieved threshold value, thermal management system should choose proper extent of forced flow and flow field in accordance with the battery pack’s current operating conditions, in order to ensure the economy of the thermal management system.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".