Fin structure and liquid cooling to enhance heat transfer of composite phase change materials in battery thermal management system
Bibliographic record
Abstract
Abstract In order to improve the performance of a battery thermal management system (BTMS) based on phase change material (PCM), expanded graphite (EG) is added to paraffin to form composite PCM (CPCM), and embedded aluminum fins are coupled with liquid cooling to enhance heat transfer. A heat generation model for lithium‐ion batteries (LIBs) is established and verified by experiments. The cooling performances of four BTMS designs were simulated. The effects of the thermal characteristics of LIBs were investigated at various velocities and directions of coolant flow as well as EG fractions in CPCMs. The simulation results indicate that Design IV shows a good cooling effect at a coolant flow rate of 0.06 m s −1 and an EG fraction of 12 wt%. Under ambient temperatures of 26°C, 35°C and 40°C, the maximum battery temperatures are 28.14°C, 37.15°C and 42.09°C, respectively, and the maximum temperature difference over the battery module is 1.88°C, 1.89°C and 1.92°C, respectively. The charge‐discharge cycle performances of the four BTMS designs were further investigated. In Design IV, the maximum temperature and the maximum temperature difference in the battery module remain unchanged during five cycles under 1, 2 and 3 C discharge rates. The new BTMS has significantly improved the secondary heat storage problem of PCMs and the temperature uniformity of LIBs. The fin structure combined with liquid cooling is efficient in enhancing the heat transfer of CPCM for battery thermal management.
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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 | 0.000 |
| 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".