Analytic–Adaptive <i>LLC</i> Resonant Converter Synchronous Rectifier Control
Bibliographic record
Abstract
Synchronous rectification (SR) is the key to achieve high efficiency for high output current LLC resonant converters. Recently proposed methods for SR control do not offer a complete solution for industrial applications. The drain-to-source voltage sensing based methods cannot utilize the full benefits of SR due to the field-effect transistor (FET) package stray inductance. The adaptive SR control methods suffer from the variable on time and cannot reach the minimum power loss. This research aims for a high-performance SR control strategy that not only has good steady-state performance but also works reliably during transients. This article introduces an analytic-adaptive method for SR control that delivers accurate adjustment for both turn on and turn off moments of SR FETs below and above resonance. The proposed method takes into account the stray inductance of SR FETs; hence, it enables full efficiency gain of SR. The feasibility of implementation for the proposed method integrated with closed-loop control is experimentally validated on a 300 W 390/12 V half-bridge LLC resonant converter with 93.66% peak efficiency.
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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".