Optimization of Power Sharing and Switching Frequency in Si/WBG Hybrid Half-Bridge Converters Using Power Loss Models
Bibliographic record
Abstract
The Si/wide bandgap (WBG) hybrid half-bridge (HHB) coordinates the hybrid-frequency operation and power-sharing ratio between the low-frequency Si phase and the high-frequency WBG phase to offer the same WBG benefits but with a much-reduced cost, in comparison to a full WBG solution. However, no generalized methodology has been reported so far to realize the full-scale optimization of switching frequency and the power-sharing ratio between those phases. In this article, we first develop a generalized power loss model for Si/WBG HHB with total power loss as output and switching frequency and power-sharing ratio as a continuous input variable. And then develop a dynamic power-sharing ratio and switching frequency control to achieve minimum power loss over a wide load range. A 3-kW prototype of the Si/SiC HHB-based dc/dc converter is built to validate the power loss model and proposed control strategy. In comparison with several fixed parameters, the dynamic parameters obtained by the proposed power loss model achieve a 6%–18% total power loss reduction without sacrificing power quality performance.
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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) | 0.001 | 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 | 0.001 |
| 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".