MétaCan
Menu
Back to cohort
Record W4404788528 · doi:10.1109/jestpe.2024.3508082

Simplified Hybrid Dual-Bridge LLC Resonant Converter With Reduced Passive Components and Loss for Emergency Power Supply

2024· article· en· W4404788528 on OpenAlexaff
Bo Hu, Jun Wang, Zipeng Ke, Jiatao Yao, Chao Zhang, Z. John Shen

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHalf bridgeDual (grammatical number)Electrical engineeringPower (physics)Resonant converterBridge (graph theory)ConvertersElectronic engineeringMaterials scienceEngineeringCapacitorPhysicsVoltage

Abstract

fetched live from OpenAlex

The proportion of passive components loss in the total power loss of inductor–inductor–capacitor (LLC) resonant converters has reached a critical point. To address this issue, this article proposes a simplified hybrid dual-bridge LLC resonant converter to reduce passive component losses. It consists of a primary LLC resonant converter and an active clamp auxiliary bridge converter connected in parallel. The turn-off current of the LLC resonant converter over the entire range is greatly reduced by means of the clamping operation of the auxiliary converter. Compared with conventional dual-bridge LLC resonant converters, the proposed converter requires only one resonant tank, which can reduce the number of passive components; therefore, it can achieve a 26.5% resonance component cost reduction and a 13.8% total volume of inductor reduction. A 1.2-kW prototype of the hybrid dual full-bridge (FB) three-leg LLC resonant converters is built as a case study to validate the proposed approach. Compared to conventional dual FB three-leg LLC resonant converters, the proposed converter can achieve a 27% reduction in passive component losses without sacrificing soft switching 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
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 integrity0.0000.001
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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Journal of Emerging and Selected Topics in Power ElectronicsSame topicAdvanced DC-DC ConvertersFrench-language works237,207