The Impact of Road Types on the Energy Consumption of Electric Vehicles
Bibliographic record
Abstract
The widespread adoption of battery electric vehicles (BEVs) is hindered by their limited ranges and long charging times. Optimizing eco-driving strategies and BEV-specific routing through a thorough understanding of the BEV discharge behavior is vital to overcome these barriers in the short term. Therefore, this study investigates the impact of road types on the BEV discharge behavior while accounting for explanatory variables (i.e., ambient temperature, the initial state of charge, and driver). Thirty participants drove a 2017 Volkswagen eGolf along two predefined routes in Rhode Island. The results illustrate that BEVs are the most efficient on-road types with medium speed and low variation (i.e., “major collectors,” “minor arterials,” and “other principal arterials”). Meanwhile, findings confirmed a significantly higher average energy consumption rate on roads with higher average speeds (“interstates” and “other freeways/expressways”). Moreover, “local roads,” associated with a low average travel speed and a high variation in speed, showed a negative effect on BEV efficiency. The study further supported previous findings that BEVs are less efficient in colder temperatures. Thus, adapting eco-driving strategies, including the alteration of route choice to avoid “local roads” and “interstates,” can offer BEV drivers the potential for energy savings and range extensions. We propose a consideration of these findings to mitigate the effects of BEV range limitations and ease BEV adoption and ownership.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".