Evaluating Unplug Incentives to Improve User Experience and Increase DC Fast Charger Utilization
Bibliographic record
Abstract
Direct current fast charging is a necessary element of the transition to electric vehicles (EVs). Regulatory complexity, capital requirements, and challenging business models hinder charging infrastructure deployment, so focusing on the efficient use of such infrastructure is of paramount importance. A tool to improve this efficiency is an incentive to terminate charging events when charging power drops, the vehicle state of charge rises above some value, or time plugged in exceeds a threshold. A timeseries charging demand model was built based on observed EV population and charging behavior. This was used to explore these three incentive trigger metrics across a range of plausible values, to find their relative impacts on the vehicles charging, those waiting in line to access a cordset, and charging site operators. Results indicate that basing such a trigger on charging power would have little impact if the threshold power is low enough to accommodate older, slower-charging vehicles, but that more restrictive limits based on state of charge or charging duration can decrease wait times, increase vehicle throughput, and increase total energy sales for cordsets serving more than 1000 EVs per year.
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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 | 0.001 | 0.002 |
| 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 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".