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Record W2060453307 · doi:10.1109/tvt.2013.2283158

Control Strategies for Wide Output Voltage Range LLC Resonant DC–DC Converters in Battery Chargers

2014· article· en· W2060453307 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 Vehicular Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaDelta-Q Technologies (Canada)
Fundersnot available
KeywordsConvertersElectrical engineeringVoltageBattery (electricity)EngineeringBattery chargerCharge pumpForward converterElectronic engineeringBoost converterCapacitorPower (physics)Physics

Abstract

fetched live from OpenAlex

In this paper, a control strategy is presented for a high-performance capacitively loaded loop (LLC) multiresonant dc-dc converter in a two-stage smart charger for neighborhood electric vehicle (NEV) applications. It addresses several aspects and limitations of LLC resonant dc-dc converters in battery charging applications, such as very wide output voltage range while keeping the efficiency maximized, implementation of the current mode control at the secondary side, and optimization of burst mode operation for current regulation at very low output voltage. The proposed control scheme minimizes both low- and high-frequency current ripples on the battery while maintaining stability of the dc-dc converter, thus maximizing battery life without penalizing the volume of the charger. Experimental results are presented for a prototype unit converting 390 V from the input dc link to an output voltage range of 3-72 V dc at 650 W. The prototype achieves a peak efficiency value of 96%.

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 score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.204
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