Variable-Frequency Critical Soft-Switching of Wide-Bandgap Devices for Efficient High-Frequency Nonisolated DC-DC Converters
Bibliographic record
Abstract
This paper derives a variable-frequency critical soft-switching control method for nonisolated DC/DC converters using wide-bandgap devices. The critical soft switching control technique under maximum frequency trajectory is introduced to maintain zero voltage switching over a wide range of modulation ratios according to the load variation. The concept prevents turn-on losses that are typically much larger than the turn-off losses in SiC and GaN FETs and the latter can be further reduced by adding external drain-source capacitors. We have derived the boundary conditions for critical soft switching operation. For the reduction of inductor value and volume, a maximum available switching frequency is applied to the converter within the constraints of device requirement and soft switching boundary conditions. We demonstrate experimentally that the proposed concept reduces the power losses in the wide-bandgap devices by a factor of approximately 3, enables an increase of the switching frequency by a factor of about 5, and a decrease of the main inductance by a factor of about 10. Then variable frequency critical soft switching control method is proposed with the constraints to maintain the maximum frequency within soft switching operation. Since our test bench uses off-the-shelf inductors, the inductors are subject to significant high frequency losses. Despite this, the converter efficiency increases by 1%.
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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) | 0.001 | 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".