Quality of service in Plug-in Electric Vehicle charging infrastructure
Bibliographic record
Abstract
Electrification of transportation is offering reduced vehicle emissions and operating costs in addition to increased energy-independence. Electric cars are anticipated to be adopted as passenger vehicles and in commercial fleets in the near future. Plug-in Hybrid Electric Vehicles (PHEVs) can drive on battery up to few hundred miles with the current battery technologies. Depleting PHEV batteries are charged from the power grid either with a Level 1 or Level 2 charger where the latter delivers more power than the former. Despite the advantages of PHEVs, charging several PHEVs simultaneously from the same distribution system may cause local outages due to transformer overloading. Thus, PHEV charging infrastructure calls for admission control schemes that operate on the smart grid. It is also essential to provide service differentiation to increase consumer satisfaction. In this paper, we propose a Quality of Service (QoS)-aware admission control scheme for the PHEV charging infrastructure. Our scheme operates on the Energy Management System (EMS) of the smart grid distribution system. The proposed approach relies on a wireless communication network that delivers the demands of PHEVs to the EMS and delivers the admission decisions of EMS to PHEVs. In our admission control scheme, PHEV owners who are willing to pay more can charge faster than the “best-effort” users similar to the Internet traffic service differentiation mechanisms. We provide mathematical analysis and simulation results for the proposed scheme. We show that high priority PHEVs are supplied with higher power rating, hence they are able to charge faster than low priority PHEVs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".