Analysis of a Hybrid Variable-Frequency-Duty-Cycle-Modulated Low-$Q$ $LLC$ Resonant Converter for Improving the Light-Load Efficiency for a Wide Input Voltage Range
Bibliographic record
Abstract
Light load efficiency and output voltage regulation of alow-Q LLC resonant converter is a critical problem for wide input voltage and load range applications. Parasitic capacitances such as rectifier diode junction capacitance (C3) degrade the soft switching performance. Compact size, high density, and high transformer turns-ratio requirements for microinverter applications add significant distributed capacitance (Cd) of the low-profile transformer, worsening the output regulation and zero-voltage-switching (ZVS) capability at light loads. Wide switching frequency requirement for regulation at light loads, which increases core losses and turnOFF switching losses in power MOSFETs, further degrades the power conversion efficiency. The conventional phase-shift modulation causes a high circulating current and loss of ZVS at light loads. Therefore, a hybrid adjustable switching-frequency-dutycycle modulation technique for improving the light load efficiency is proposed and analyzed for a full-bridge LLC resonant converter. Accurate loss analysis for the proposed modulation scheme, including the effect of parasitic capacitances, is performed using time-domain equations. The proposed methodology precalculates the optimal duty cycle at light load conditions for the required input voltage range such that minimum power losses are incurred. Variation in switching frequency at the preselected duty-cycle value regulates the output voltage. ZVS over a wide range of operating conditions is observed. An experimental prototype for a 20-40 V input, 380-V/300-W output LLC converter is tested for the validation of theoretical analysis.
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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 | 0.000 | 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".