Observational Evaluation of the Maximum Practical Utilization of Electric Vehicle DCFC Infrastructure
Why this work is in the frame
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Bibliographic record
Abstract
Central to the design of a direct current fast charging (DCFC) network is the question of how much energy a DCFC of a given power can supply to vehicles without users being forced to queue to charge. We define ‘utilization factor’ as the ratio of the energy delivered by a DCFC in a multi-day period to the maximum amount of energy it could deliver in period. Three and a half years of data from 12 DCFCs are examined, characterizing each charging event by both the utilization factor and the time lag since the termination of the previous charging event. Short lags between events are inferred to indicate queuing. To keep the fraction of would-be users who have to queue below 10%, the overall utilization of the DCFC must likewise be limited to 10% (or 7–17% in exceptionally heterogeneous or exceptionally homogeneous traffic patterns, respectively). E.g., a 100 kW DCFC should not be expected to deliver more than 240 kWh per day (100 kW × 24 h × 10%).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| 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) | 0.001 | 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