Design, implementation and analysis of an advanced digital controller for active virtual ground-bridgeless PFC
Bibliographic record
Abstract
The paper presents a new digital control scheme for Active Virtual Ground-Bridgeless PFC (AVG-BPFC) in electric vehicle wireless charging application which is able to suppress the common-mode (CM) voltage induced from the conventional BPFC, where the best optimized solution between the system efficiency and Electromagnetic Interference (EMI) performance in the PFC state can be obtained. However, a resonant characteristic will be presented in the current waveform due to the converter form a LCL filter structure. It leads the controller design in the AVG-BPFC to become challenge. Thus, a triple loop control's architecture is proposed where inner loop is designed with boundary controller to eliminate the filter resonance and to control the AC voltage. In the mid-loop, deadbeat controller is applied to maximize the power quality by offering a precise current tracking function. A PI controller is adopted in the outer loop to regulate the output voltage. Such control scheme is implemented digitally on a 1.5 kW prototype. In the paper, theoretical models of the whole system are analyzed and the system performance is successfully verified in both of the steady state and transient state conditions. The experimental results show a good agreement with the theoretical knowledge.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".