A High Conversion Ratio Quasi-Resonant Flying Capacitor DC-DC Converter
Bibliographic record
Abstract
A new single-stage wide-range DC-DC converter topology that is well suited for emerging high step-down low-power applications is presented. The topology fully utilizes the energy stored in the flying capacitor thus minimizing its size compared to other flying capacitor-based solutions. Depending on the load value, the converter can operate in either continuous or discontinuous inductor current conduction while providing soft switching. These advantages are achieved by having a discontinuous voltage across the flying capacitor. It is charged from zero to the full input voltage. Thus, the switching losses, which are the largest contributors to the overall losses in high step down low current applications, are minimized.The effectiveness of this single-stage converter is verified with an experiment silicon-based prototype, providing up to 6.5 A of current at the output and operating with various conversion ratios. For operations at 48V-5V, 48V-3.3V, 48V-1V and 24V-1V the topology achieves a peak efficiency of 92%, 90%, 84% and 88%, respectively. In the targeted low power range, and to the best of our knowledge, the efficiency of the introduced topology is comparable to the arguably most efficient single-stage GaN based solutions [1], without a penalty paid in the overall volume of the converter.
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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".