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Record W4312306406 · doi:10.1109/jestpe.2022.3226196

Optimization of Power Sharing and Switching Frequency in Si/WBG Hybrid Half-Bridge Converters Using Power Loss Models

2022· article· en· W4312306406 on OpenAlexaff
Chao Zhang, Xufeng Yuan, Jun Wang, Bo Hu, Xin Yin, Z. John Shen

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsSimon Fraser University
FundersNational Natural Science Foundation of China
KeywordsPower (physics)ConvertersPower controlHybrid powerElectronic engineeringControl theory (sociology)Automatic frequency controlComputer scienceMaterials scienceElectrical engineeringEngineeringControl (management)Physics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.231
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

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