Design and Simulation of a Thermal Management System for Plug-In Electric Vehicles in Cold Climates
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This article presents an integrated thermal and dynamic model of Electric Vehicles (EV) to assess the effect of implementing a passive heating method on increasing the electric range of a typical light-duty electric vehicle in cold climates. By introducing passive thermal storage using phase change materials (PCM) temperature of the vehicle's compartment is maintained at certain set point for comfort. Thermal model uses the overall heat transfer coefficient from the compartment to the ambient in cold weather and assumes uniform temperature distribution in the compartment. We use real-world driving, parking and estimated probability of charging for more than 10 thousand daily duty cycles recorded in the city of Winnipeg, Manitoba, Canada. We simulate driving a typical light-duty electric vehicle (EV), with 24 kWh of battery storage over 44 million data points of the database in low temperatures ranging from 0°C to -20°C. While the EV is plugged in, PCM-based heat storage absorbs heat generated by an electric heater, also connected to the electric grid, to change phase. Based on the results of the simulation, inclusion of PCM in the seat cushions can help to maintain the temperature of vehicle's compartment constant at 15°C for an increase of the electric range up to 21%.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".