Adaptive Power Sharing and Switching Frequency Control for Power Loss Optimization in WBG/Si Hybrid Half-Bridge Converters
Bibliographic record
Abstract
The optimization of switching frequencies and the power-sharing ratio between the silicon (Si) phase and wide-bandgap (WBG) phase are critically important for the efficiency improvement and safe operation of the WBG/Si hybrid half-bridge (HHB)-based power converters. In this article, a novel adaptive power sharing and switching frequency control for power loss optimization in WBG/Si HHB is proposed. It is based on intelligence particle swarm optimization (PSO), especially suitable for applications with time-varying operation current. The PSO approach only evaluates the fitness values to seek the best optimal parameters without requiring an accurate power loss model and additional hardware components. Therefore, the proposed method can be easily implemented and adapted to various working conditions. A 3-kW prototype of the Si/SiC HHB-based single-phase inverter is built to validate the proposed approach. In comparison with the fixed frequency and power-sharing ratio, the proposed method achieves an 18% maximum total power loss reduction while maintaining the same 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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".