An Enhanced Control Strategy for Three-Phase PFC Converter Integrated with DAB-Based Level-3 EV Chargers Under Non-Ideal Grid Conditions
Bibliographic record
Abstract
This paper proposes an enhanced control strategy for a three-phase, two-level power factor correction (PFC) rectifier cascaded with a dual-active-bridge (DAB) DC-DC stage for Level-3 electric vehicle (EV) chargers. High-power fast chargers require a near-unity power factor and compliance with IEEE-519 harmonic standards. The proposed system employs silicon-carbide (SiC) MOSFETs and a voltage-oriented control framework with a Type-II compensator for outer DC-link regulation and synchronous dq-frame current control. Space Vector PWM (SVPWM) is implemented using a projection-based algorithm that eliminates trigonometric computations and sector detection by directly projecting the reference vector with symmetric zero-vector timing, resulting in a compact, real-time-friendly solution. The stabilized DC-link voltage drives the DAB converter under single-phase shift modulation. Real-time simulation studies on OPAL-RT demonstrate fast transient response, IEEE-519 compliant current quality, and near-unity power factor. The proposed controller outperforms conventional PI-based SRF methods and maintains robust performance even under non-ideal grid conditions, confirming its suitability for high-power EV charging.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".