A QoS Scheme for Charging Electric Vehicles in a Smart Grid Environment
Bibliographic record
Abstract
Electric vehicles (EVs) are expected to greatly reduce the carbon emissions from surface transport if they are widely used and efficiently charged. One of the main limitations of EVs is their limited range and relatively long recharging times. This limitation is closely associated with the current battery technologies used in the EVs. In order efficiently utilize the EVs, their charging schedules and locations must be effectively integrated within the smart grid. Real-time and reliable integration of EVs with the smart grid could solve problems related to demand response, cost and time of charging. In this paper, we propose a Quality of Service (QoS) scheme for Charging EVs (QCEV) in a smart grid environment. The proposed scheme provides centralized QoS differentiation to EVs that are communicating with an Access Point (AP) in situations where immediate EV battery charging is required. Our simulation results show that QCEV could significantly improve the performance of the wireless communication network especially in dense deployments.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".