A novel V2V charging scheme to optimize cost and alleviate range anxiety
Bibliographic record
Abstract
Nowadays, electric vehicles (EVs) as a source of clean energy are expected to replace vehicles with internal combustion engines. However, the high cost of installation has prevented the development of charging infrastructure to meet the rising number of EVs. Furthermore, the immature and uneven distribution of charging stations (CSs) has resulted in a lack of charging infrastructure in regions such as highways and rural areas. Therefore, due to range anxiety and a lack of CSs, the adoption of EVs is increasing these days steadily. To address these issues, the new concept of vehicle-to-vehicle (V2V) charging has received attention. In this paper, one algorithm for V2V power charging is proposed to support V2V power exchanges in a distributed power system with both demander and supplier EVs separated into a number of zones, where V2V power exchanges are optimal for matching EVs together to trade energy. In our algorithm, a Hungarian matching algorithm is used to match EVs so that the cost of demander EVs is minimized. The simulation results stem from realistic parameters obtained from real-life data for the suggested methodology is compared to the traditional method of assigning EVs to CSs, and the findings reveal that our efficient, realistic, and practical V2V matching algorithm presents an intelligent and complete framework for managing and allocating energy between EVs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".