Experimental investigation of heat pipes and liquid cooling based hybrid Battery Thermal Management System
Bibliographic record
Abstract
The thermal management system (TMS) for lithium-ion (Li-ion) batteries in electric vehicles (EVs) is an essential requirement to ensure their smooth operation due to the high temperature generated by the batteries during high C rate charging or discharging.In the current study, this paper presents an analysis of the performance of a heat pipe-assisted hybrid cooling battery thermal management system (BTMS) for electric vehicle (EV).Combining a cooling channel and round heat pipes (RHP) at system level, this study examines the vertical position of the RHP under various heat load conditions and liquid temperatures.Moreover, the study also goes through thermal resistance network of the entire system and determines the part with high temperature gap.Experimental results demonstrated that the current design with heat pipes in vertical position is capable of transferring heat released at 1.126× 10 6 𝑊/𝑚 3 (10W) from heater cartridges.This was enough to keep the battery surface temperature below 59°C and the difference in temperature between them under 2°C.Furthermore, the heater cartridge surface temperature showed 39°C when 5W heat power was released.The two test cases were conducted at 0.33 L/min and 20°C water temperatures which is the average ambient temperature.Finally, it should be noted that the decrease in the temperature of the water from the cooling tower is proportional to the decrease in the temperature of both ends of the heat pipe.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".