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Record W4412352671 · doi:10.1109/tpel.2025.3588426

A Simplified Time-Domain Model-Based Maximum Efficiency Tracking-Aided Synchronous Rectification Strategy for <i>CLLC</i> Chargers

2025· article· en· W4412352671 on OpenAlexafffund
Ruizhi Wei, Haoran Wang, Gregory J. Kish, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRectificationDomain (mathematical analysis)Automotive engineeringTracking (education)Computer scienceElectronic engineeringTime domainControl theory (sociology)Control engineeringEngineeringElectrical engineeringMathematicsVoltageArtificial intelligence

Abstract

fetched live from OpenAlex

Synchronous rectification (SR), achieved by replacing secondary-side diodes with active <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> channels, is crucial for reducing conduction losses in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CLLC</i> converters. Traditional SR methods, however, either entail costly and bulky hardware or rely on complex mathematical models. This article introduces a simplified time-domain model (STDM) that utilizes mathematical principles and detailed operational assumptions to provide initial duty cycles and gate signal phases for SR under various frequencies and load conditions. However, the STDM's overall accuracy is compromised by parasitic parameters and parameter tolerance of the resonant tank. Directly employing the STDM-based SR method may lead to hard-switching of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small>s or increased circulating current, reducing system efficiency. To mitigate this issue, a maximum efficiency tracking (MET)-aided SR signal adjustment method is proposed, which further modifies the duty cycle and phase shift to minimize the current passing through the diodes. This STDM-MET-SR approach does not necessitate high-bandwidth sensors or complex control algorithms while offering immunity to parasitic parameters and system parameter variations. Experimental results demonstrate that the STDM-MET generated SR signals contain negligible errors compared to the required SR signals, and the efficiency of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CLLC</i> converter with STDM-MET-SR implemented is significantly improved compared to those of diode rectification. Overall, the proposed STDM-MET-SR approach offers a cost-effective and efficient solution to reduce conduction losses in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CLLC</i> converters.

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: none
Teacher disagreement score0.988
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

Explore more

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