Energy Consumption of a Battery Electric Vehicle in Winter Considering Preheating: Tradeoff Between Improved Performance and Total Energy Consumption
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".