Optimization of the Cooling Performance of Symmetric Battery Thermal Management Systems at High Discharge Rates
Bibliographic record
Abstract
An air cooling battery thermal management system (BTMS) is widely used in electric vehicles (EVs). In this work, numerical simulation is used to provide a greater understanding of the system efficiency of T-type symmetric air cooling BTMS (T-BTMS) and its coupled systems. For T-BTMS, the method of the coefficient of variation (MCV) is applied to evaluate the schemes with different inlet flow rates and battery clearances. It is found that optimum cooling performance and energy consumption are achieved at an internal clearance of 3 mm and an inlet air velocity of 6 m·s –1 . Further improvements in cooling performance can be realized by introducing heat transfer fins into the module. Increasing the number of fins can improve cooling performance but results in higher energy consumption. To further improve the cooling performance at high battery discharge rates, a coupled system with air cooling and liquid cooling is proposed. It is found that, at high discharge rates, the coupled system gives better cooling performance and lower energy consumption compared to uncoupled systems. When the battery module is fully discharged at the rate of 4 C, the maximum temperature of the coupled system is below the critical value of 45 °C and the maximum temperature difference is less than 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 | 0.000 | 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 | 0.000 | 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".