Novel Hybrid Cooling Concept for Battery Thermal Management Design
Bibliographic record
Abstract
Electric vehicles (EVs) is one of the most promising solutions to global warming and fossil fuel crisis.The key to EV development is the battery.Li-ion battery has been the most popular choice in the industry for a variety of advantages over the others.However, battery performance is heavily related to its working temperature, and the health of battery a pack in long-term is hugely affected by temperature uniformity.The optimal working temperature ranges from 25℃ to 40℃, while the temperature non-uniformity should be lower than 5℃.A novel hybrid cooling concept for battery applications is proposed and experimentally proved in this study.The concept utilizes any combination of conductive, convective, and evaporative phase change cooling effects.The concept takes nearly no extra power from a normal air cooling method by using capillary effect as the driving force of water coolant, but achieves a higher cooling efficiency and better temperature uniformity.The concept may recycle A/C condensate so the water coolant could be instantly consumed at its generation and hence adds no extra weight to the vehicle.The air and water coolant after use will be released to the ambient without harming the environment.The results show a higher cooling efficiency and better temperature uniformity over the pack.The concept was able to retain both of the maximum working temperature and non-uniformity under the limits.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".