Influence of Battery Energy, Charging Power, and Charging Locations upon EVs’ Ability to Meet Trip Needs
Bibliographic record
Abstract
One year of high-resolution driving data from a sample of 333 instrumented gasoline passenger vehicles are used to create a trip inventory of U.S. vehicle travel requirements. A set of electric vehicles (EVs) is modeled, differing in battery size (kWh), recharging power (kW), and locations for charging when parked. Each modeled EV’s remaining energy is tracked while traversing the entire sample’s trip inventory in order to estimate how well each EV meets all U.S. driving requirements. The capital cost of refueling infrastructure is estimated per car, for gasoline and for each analyzed combination of charging station locations. We develop three metrics of the ability of different EV characteristics to meet trip requirements: the percentage of trips successfully met by each modeled EV, the number of days that the driver must “adapt” EV use to meet more demanding trip requirements, and the total driver time required for refueling. We also segment the market of trip patterns per car, finding that 25% to 37% of the vehicle population could meet all their drivers’ trip needs with a smaller-battery EV combined with community charging. This potential combination of EVs and charging would enable lower-price EVs and lower-cost recharging power, and would broaden EV availability to groups for whom today’s EVs and charging configurations are less accessible.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".