A Line Cycle Synchronous Rectification Strategy Based on Time-Domain Analysis for Single- Stage AC–DC LLC Converters
Bibliographic record
Abstract
Synchronous rectification that is being widely used in high power and current DC-DCLLCresonant converters to reduce conduction losses can be challenging in single-stage AC-DCLLCconverters with high output voltage levels (i.e., >200 V) where synchronous rectifier (SR) driving ICs cannot be used. In this paper, a simple AC line cycle synchronous rectification strategy with direct control by a cost-effective microcontroller unit (MCU) is proposed for single-stage AC-DCLLCconverters with high switching frequencies using wide bandgap devices (i.e., GaN or SiC). The SR gate pulse is generated based on the time-domain calculated conduction time, which is then switchedonandoffover the AC line cycle to avoid reverse power flow in light load conditions. The proposed strategy reduces the complexity of implementation over any adaptive online calculation or model-based methods that require powerful and expensive MCUs. First, the operation is briefly described followed by the time-domain analysis for AC operation. Next, the calculation and methodology behind the proposed AC line cycle SR driving strategy are discussed in detail. A scaled-down wide bandgap-based AC-DCLLCconverter prototype with a 250--400 V output voltage range is used with digital control implementation to validate the performance of the proposed synchronous rectification strategy. It is found that maximum efficiency of 98.1% can be achieved which is improved by around 0.5% over the conventional fixed conduction time method. Moreover, it is shown that the proposed method obtains the same efficiency levels as more complex adaptive SR driving approaches.
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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) | 0.003 | 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".