Mobility impact on the performance of electric vehicle-to-grid communications in smart grid environment
Bibliographic record
Abstract
Plug-in Electric Vehicles (PEVs) are expected to be widely utilized in the near future if issues related to the availability of charging infrastructure are resolved and if PEVs are efficiently integrated with the smart grid. The Vehicle-to-Grid (V2G) system is an emerging technology that enables the communication and control between PEVs and the smart grid. This promising concept is designed to provide the vehicles with information about where and when to charge their batteries, and allows the smart grid to acquire power from a PEV. An essential element to the success of V2G systems is reliable and secure communication system. Wireless communications in highly mobile V2G environment introduce serious challenges, such as reliability and real-time communication. In this paper, we present a comprehensive analysis of the impact of speed on the end-to-end delay and throughput in V2G communication scenarios. We focus on situations where authentication is performed when essential information such as payment data is exchanged between PEVs and its charging infrastructure. Furthermore, we present realistic delay analysis of the proposed communication infrastructure. Our simulation results show the impact of traffic density and speed on both the end-to-end delay and the throughput. We draw recommendations based on our test scenarios and simulation results.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".