Numerical Modeling of Submodule Heat Transfer With Phase Change Material for Thermal Management of Electric Vehicle Battery Packs
Bibliographic record
Abstract
In this paper, passive thermal management of an electric vehicle (EV) battery pack with phase change material (PCM) is studied numerically. When the temperature in the cells increases, and consequently in the submodule also, the heat is absorbed through melting of the cooling jacket which surrounds the cells. This, in turn, creates cooling effects in the cell and the battery pack. A finite volume based numerical model is used for the numerical simulations. The effects of different operating conditions are compared for the submodule with and without the PCM. The present results show that a more uniform temperature distribution is obtained when the PCM is employed which is in agreement with past literature and experimental data. The results also imply that the effect of PCM on cell temperature is more pronounced when the cooling system operates under transient conditions. The required time to reach the quasi-steady state temperature is less than 3 h, and it strongly depends on the heat generation rate in the cell. The maximum temperature of the system decreases from 310.9 K to 303.1 K by employing the PCM and the difference between the maximum and minimum temperatures in the submodule decreases in this way. The temperature differences are 0.17 K, 0.68 K, 5.80 K, and 13.33 K for volumetric heat generation rates of 6.885, 22.8, 63.97, and 200 kW/m3, respectively.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".