VANET based online charging strategy for electric vehicles
Bibliographic record
Abstract
An online charging strategy is an efficient approach to provide charging plans for electric vehicles (EVs) from a global point of view, aiming to improve energy efficiency while avoiding overloading of an electric power system. However, designing an efficient online charging strategy to achieve optimal energy utilization remains a challenging problem, especially when the coordinated behaviors of both EVs and charging stations are taken into consideration. In this paper, we first introduce an intelligent power distribution system which utilizes vehicular ad-hoc networks (VANETs) to enable communication among EVs on roads, road-side units (RSUs), and a vehicle-traffic server. Then, we propose a globally optimal online EV charging strategy, which not only improves energy utilization of the whole system but also prevents charging stations from overloading, which may cause a voltage drop in the power distribution system. Lagrange duality optimization techniques are exploited to address the associated optimal EV charging problem. The performance of our proposed strategy is evaluated by extensive simulations, and the results are compared with that of the traditional autonomous offline charging strategy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".