A Soft-Switched High-Conversion-Ratio Quasi-Resonant Flying Capacitor DC–DC Converter
Bibliographic record
Abstract
This article presents a new flying capacitor-based quasi-resonant DC-DC converter topology for high step-down, low-power applications, ranging from fractions of a watt to tens of watts. The converter has a structure similar to that of a conventional three-level flying capacitor buck, but it operates in a fundamentally different manner, offering a favorable trade-off for many targeted applications. By allowing a small flying capacitor to fully charge and discharge between 0 and$V_{in}$, using a unique switching scheme, switching losses are drastically reduced at the expense of requiring transistors rated for the full input voltage. The converter can operate in both discontinuous conduction mode (DCM) and continuous conduction mode (CCM). In DCM, soft switching is achieved on all edges, independent of operating conditions, while in CCM, soft switching occurs on most edges. The capacitor charging process causes the converter to draw a fixed amount of energy per switching cycle, resulting in a unified small-signal model and a transfer function with a$Q_{o}$factor limited to$1/\sqrt{2}$, simplifying voltage-mode compensator design compared to standard buck solutions. The effectiveness of the introduced solution is verified through simulations and experimental prototypes, processing up to 50 W of power with varying inductor values. These prototypes were designed to explore trade-offs for different applications while maintaining a low converter volume and high power processing efficiency. Experimental results for a 48 V-to-1 V/4 A converter demonstrate peak efficiencies of 81.74% at 3 W, 87.36% for 48 V-to-2 V/10 A at 6.4 W, 89.87% for 48 V-to-3.3 V/10 A at 10.6 W, and 91.33% for 48 V-to-5 V/10 A at 16 W.
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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".