Synchronous Rectification of LLC Resonant Converters Using Homopolarity Cycle Modulation
Bibliographic record
Abstract
In order to further reduce losses in LLC resonant converters, the use of synchronous rectifiers (SRs) in the output rectifier is desirable. Analysis and detection of the conduction angles of the SRs used in an LLC converter are the main challenges in synchronous rectification. This paper develops a new time-domain theoretical analysis, called homopolarity cycle, enabling synchronous rectification in LLC resonant converters with the use of a low-cost polarity-based sensing technique. The homopolarity cycle considerably reduces the complexity of the LLC converter's analysis, relates the conduction angles of the SRs to the gate driving signals of the inverter switches and the polarity of the rectifier voltage, and finally introduces a low-cost polarity-based sensing technique. A synchronous rectification control algorithm, called homopolarity cycle modulation (HCM), is proposed, which only needs to sense the polarity of the rectifier voltage, which is simple to measure and immune to noise. A simple graphical plane, named the homopolarity, is introduced to provide information about the SRs' conduction angles. The proposed HCM method is validated by experimental and simulation results. Unlike the conventional synchronous rectification technique, the results have shown that the proposed HCM synchronous rectification method has a flat synchronous rectification coverage from light to full loading conditions while using a simple sensing strategy.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".