A novel heat dissipation structure based on flat heat pipe for battery thermal management system
Bibliographic record
Abstract
Flying car is an effective transport to solve current traffic congestion. The power batteries in flying cars discharge at a high current rate in the takeoff and landing phase, evoking a severe thermal issue. Flat heat pipe (FHP) is a relatively new type of battery thermal management technology, which can effectively maintain the temperature uniformity of the battery pack. We have constructed a resistance-based thermal model of the batteries considering the impact of the state of charge (SOC), battery temperature, and current on the battery heat generation. The FHP model is developed based on segmental heat conduction model, and integrated into the battery model to form the battery-FHP-coupled model for a battery module. Experiments are carried out to verify its accuracy. Then, the battery thermal performance is analyzed under the different discharging conditions including constant discharge rates and dynamic discharge rates for flying cars. Under the condition of the flying cars, the battery maximum temperature appears at the end of takeoff stage, while the maximum temperature difference appears during the forward flight segment. Moreover, different FHP heat dissipation structures are studied to further improve the battery thermal performance. The configuration with the best performance is adopted for the battery pack, and it can meet the heat dissipation requirements of the pack at a discharge rate of 3C or that of flying cars. Finally, the influence of inlet cooling air velocity and temperature on battery thermal performance is investigated. According to the research results, air velocity has little effect on the battery maximum temperature at the discharge rate of flying cars, but it can obviously affect the temperature decrease rate. Besides, the battery maximum temperature and its temperature difference develop linearly with the air temperature.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".