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Record W2319962468 · doi:10.1109/tpel.2014.2313130

Bang-Bang Charge Control for LLC Resonant Converters

2014· article· en· W2319962468 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.

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsConvertersCapacitorBang–bang controlVoltageBandwidth (computing)Control theory (sociology)Electronic engineeringSwitching frequencyAutomatic frequency controlRLC circuitSwitched capacitorEngineeringElectrical engineeringComputer scienceControl (management)Optimal controlTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

This paper proposes a Bang-Bang Charge Control (BBCC) method for LLC resonant converters. Instead of switching frequency control, the BBCC method utilizes the series resonant capacitor voltage level to trigger the MOSFETs' switching actions. Advantages of the proposed method include fast dynamic performance and simple implementations. Small-signal analysis shows that the loop bandwidth can achieve around 1/6 of the switching frequency in all operating conditions. Experimental results show that 5 to 25 A load step transients are completed within only seven switching cycles at both 400- and 300-V input-voltage conditions. Practical considerations are also discussed in detail.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
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
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.198
Teacher spread0.193 · 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