Small Signal Analysis and Closed-Loop Design of Constant Frequency Operated Single-Stage Bidirectional PFC-Based Converter for Plug-in G2VEV Charger
Bibliographic record
Abstract
This paper describes a comprehensive small-signal analysis and closed-loop design of a single-stage isolated AC-DC converter with power factor correction (PFC) that is intended for use in charging Electric Vehicles (EVs) application. To achieve this, a state-space averaging method is utilized to derive a small-signal model, and a modified control technique is developed to enable soft-switching without the need for additional snubber circuits or passive circuitry. By utilizing this model, the proposed converter can be operated with fixed frequency bidirectionally in a four-quadrant operation, allowing for both active and reactive power transfer between the grid and the battery of EV, which helps to stabilize the grid. To ensure the functionality of the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes of operation, a controller is designed based on the small-signal mathematical model, and its performance is evaluated using simulation PSIM 11.04 software. The results show that the proposed topology performs as per theoretical analysis and its claims, and also this is confirmed by experimental tests carried out on a 1.5 kVA hardware prototype.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".