Numerical modeling and experimental investigation of a prismatic battery subjected to water cooling
Bibliographic record
Abstract
In this paper, a numerical model using ANSYS Fluent for a minichannel cold plate is developed for water-cooled LiFePO4 battery. The temperature and velocity distributions are investigated using experimental and computational approach at different C-rates and boundary conditions (BCs). In this regard, a battery thermal management system (BTMS) with water cooling is designed and developed for a pouch-type LiFePO4 battery using dual cold plates placed one on top and the other at the bottom of a battery. For these tasks, the battery is discharged at high discharge rates of 3C (60 A) and 4C (80 A) and with various BCs of 5°C, 15°C, and 25°C with water cooling in order to provide quantitative data regarding the thermal behavior of lithium-ion batteries. Computationally, a high-fidelity computational fluid dynamics (CFD) model was also developed for a minichannel cold plate, and the simulated data are then validated with the experimental data for temperature profiles. The present results show that increased discharge rates (between 3C and 4C) and increased operating temperature or bath temperature (between 5°C, 15°C, and 25°C) result in increased temperature at cold plates as experimentally measured. Furthermore, the sensors nearest the electrodes (anode and cathode) measured the higher temperatures than the sensors located at the center of the battery surface.
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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 | 0.001 | 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".