An Experimental Study of Battery Thermal Management using Air Cooling and PCM (Lauric Acid)
Bibliographic record
Abstract
This report is on a Thermal management system using air and PCM (Lauric acid) as an electric vehicle cooling module. Hybrid and electric vehicles are emerging with great technology in today’s world, a lot of challenges are being faced by all the manufactures, one of the main problems is the battery thermal management system. Battery thermal management system (BTMS) maintains a standard temperature for the battery to work efficiently. Cooling the battery using air and phase change material (PCM) is the latest and most efficient way of cooling the battery. This enhancement is possible by using CPU fans to direct the atmospheric air to focus and cool the width of the battery through the battery compartment’s air vents. PCM cooling is achieved by using 30/70 mixture of water and Lauric acid respectively, PCM is run around both sides of the battery’s length through copper tubes in which PCM is pumped using a submersible 12v DC pump. DC pump is turned ON and OFF by the Arduino Nano micro-controller and temperature sensor connected to the battery detects the temperature of the battery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.004 | 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".