Experimental Study of a Thermal Cooling Technique for Cylindrical Batteries
Bibliographic record
Abstract
Abstract Lithium-ion (Li-ion) batteries have been considered the most promising power source for road transportation. However, the performance and lifespan of Li-ion batteries are strongly dependent on the working temperature. The optimal working temperature is usually within a narrow range, from 25 to 40 °C, and the non-uniformity is usually required to be lower than 5 °C. Therefore, the industry is seeking a thermal management system that is lightweight, simple-structure, energy-saving, and environmentally friendly. Air-cooling, liquid-cooling, and phase-change material (PCM) are the three most common cooling methods in the literature. In this study, a new concept of hybrid-cooling which utilizes all the three cooling methods is proposed. The concept can use either normal tap water or the condensate from a vehicle’s air-conditioner as the coolant source. Also, the coolants can be released back to the ambient environment instead of a coolant recirculation system to reduce weight and complexity. The concept was studied in detail experimentally using the 26,650 Li-ion batteries. The results indicate that the proposed hybrid-cooling concept reduced the maximum surface temperature by about 83%, 70%, and 57% compared with the other three cooling methods: the no-cooling, air-cooling, and water-cooling test results, respectively. Additionally, the concept successfully maintained the temperature uniformity below the recommended 5 °C.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".