Energy Consumption of a Battery Electric Vehicle in Winter Considering Preheating: Tradeoff Between Improved Performance and Total Energy Consumption
Why this work is in the frame
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Bibliographic record
Abstract
The driving range of battery electric vehicles (BEVs) is greatly influenced by ambient conditions, especially at low temperatures. To address this, the battery and the passenger cabin can be preheated using energy from the electric grid. This is regarded as a strategy to reduce the energy consumption of these vehicles in winter. For long trips, preheating can indeed be translated into a slight increase in the driving range. However, for short trips, the amount of energy saved from the battery does not outweigh the additional energy demanded from the grid. This article aims to quantify the increase of the driving range and of the total energy consumption with preheating. To do this, a simulation of the interconnection of the main subsystems of a BEV is used. The results for an ambient temperature of –10 °C, 45 min of preheating, and a normalized extra-urban driving cycle show an increase of 8.5 km of the driving range for a long trip and an increase of 17% of the total energy consumption for a 1-h trip.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it