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Analysis and Design of a Single-Phase Bridgeless Cuk-based PFC Converter as On-Board Charger with Reduced Number of Components and Losses

2019· article· en· W3021162021 on OpenAlexaff
Sivanagaraju Gangavarapu, Akshay Kumar Rathore, Santanu Mishra, Rajeev Kumar Singh

Bibliographic record

Venue2019 IEEE Transportation Electrification Conference (ITEC-India) · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsĆuk converterBuck–boost converterBoost converterSINADRBuck converterFlyback converterRobustness (evolution)Power factorForward converterElectronic engineeringVoltageControl theory (sociology)EngineeringComputer scienceElectrical engineeringControl (management)Chemistry

Abstract

fetched live from OpenAlex

In this paper, a new single-phase bridgeless Cuk-based power factor corrected (PFC) converter with reduced number of components for on-board charging application is presented. The converter is operated in discontinuous current conduction mode to achieve natural PFC at AC input, and thereby sensing of input voltage and input current is avoided, which makes the converter operation cost-effective, and increases the converter robustness to high-frequency noise. The converter control is quite simple, and requires only one control loop, and one sensor. The proposed converter requires less components, and the components voltage stress is less when compared to the conventional Cuk converter, which reduces the components switching losses and enhances the converter efficiency. Further, only one semiconductor is in current flowing path for throughout the converter operation. Thus, this will reduce the conduction losses, and further it eases the converter thermal management. The converter detailed steady state operation for one switching cycle is presented, and the design expressions for each passive component are derived to simplify the converter design. The proposed converter analysis and the design are confirmed with the simulation results from PSIM software.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
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
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.248
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 designBench or experimental
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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

Same venue2019 IEEE Transportation Electrification Conference (ITEC-India)Same topicAdvanced DC-DC ConvertersFrench-language works237,207