Modeling of bidirectional electric vehicle charger for grid ancillary services
Bibliographic record
Abstract
Abstract This paper presents the evolution of bidirectional electric vehicle charger for G2V (i.e., Grid-to-Vehicle) and V2G (i.e., Vehicle-to-Grid) utility. Electric vehicle are growing day by day in the area of transportation. As more electric vehicles hit the road to maintain the need for charging, bidirectional charging becomes essential. Bidirectional charging in electrical vehicles facilitates users to either make energy flow to the vehicle or flow from the vehicle. The proposed design allows users to make economic gains from electric vehicle with other exciting advantages. Particularly unidirectional charging solution restricts the user to use energy for charging only applications whereas, this paper proposed a control strategy for bidirectional charging of electric vehicles. Hence electric vehicles could be potentially used as a source during an emergency like power outages, grid failure, or whenever there is an excess load on grid and user needs more energy. Additionally, this paper uses an integration of buck and boost converter to develop a bidirectional vehicle charger. The performance of bidirectional converter with control algorithm is verified by simulation on MATLAB Simulink.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".