Quality of service in Plug-in Electric Vehicle charging infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it