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Record W2940386646 · doi:10.1109/tpel.2019.2909903

MOSFET Power Loss Estimation in <i>LLC</i> Resonant Converters: Time Interval Analysis

2019· article· en· W2940386646 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTopology (electrical circuits)ConvertersDiodePower (physics)Electronic engineeringComputer scienceMOSFETInverterElectrical engineeringEngineeringVoltagePhysicsTransistor

Abstract

fetched live from OpenAlex

In the past ten years, LLC resonant converters have become a mainstream topology for dc/dc power conversion, and multiple design tools have been developed for this topology, including controllers, regulators, soft-switching techniques, etc. While many tools are available for designing this converter, techniques for accurately determining power losses in the inverter MOSFETs of the topology based on time-domain analysis have not been fully explored yet. Precise power loss estimation is fundamental to determine the thermal behavior of the switches before the converter is built, which accelerates and optimizes the thermal management design process. In addition, accurate methods of estimating conduction losses, which are dominant in this topology, switching losses, and body diode losses are lacking in the literature. This paper proposes a method for enhancing power loss estimation in LLC inverter MOSFETs based on time-domain analysis of the converter. Moreover, a detailed characterization of MOSFET's conduction losses (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cond</sub> ), switching losses (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sw</sub> ), and body diode losses (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">diode</sub> ), including the effects of different parameters such as gate-source voltage (V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GS</sub> ), junction temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> ), drain current (I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> ), and drain-source voltage (V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DS</sub> ), is presented, which can further improve power loss assessment in this topology. The developed method based on time interval analysis replaces the simplistic first-harmonic approximation (FHA), which allows for improved power loss calculations. Further improvement is obtained with the detailed characterization of the switching device. As verified by simulation and experimental results, the proposed estimation tool provides a significant boost in accuracy for power loss determination when compared to the existing method for power loss estimation using FHA.

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.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.884
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.003
GPT teacher head0.200
Teacher spread0.197 · 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