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Record W2045598693 · doi:10.1109/apec.2014.6803526

A hybrid resonant bridgeless AC-DC power factor correction converter for off-road and neighborhood electric vehicle battery charging

2014· article· en· W2045598693 on OpenAlexaff
Md. Muntasir Ul Alam, Wilson Eberle, Fariborz Musavi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsDelta-Q Technologies (Canada)University of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsBoost converterElectrical engineeringPulse-width modulationConvertersElectric vehicleVoltageDiodeElectronic engineeringPower (physics)Computer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, a new hybrid resonant bridgeless ac-dc PFC boost converter operating in continuous-conduction-mode (CCM) is proposed for neighborhood electric vehicle (NEV) battery charging. This hybrid-resonant boost converter has two active switches that operate in pulse-width modulation (PWM) and hybrid-resonant modes of operation. This combined modulation technique is called hybrid resonant PWM (HRPWM). The proposed converter with HRPWM features several benefits. The gates of the two active switches are tied together, so, the converter does not need any extra circuitry to sense the positive, or negative line-cycle operation. The semiconductor devices operate with a voltage stress close to the output voltage. The resonant tank components are relatively small in size and the hybrid-resonant mode of operation alleviates the reverse recovery losses for the body diode. The converter architecture exhibits less common mode noise compared with the dual-boost and bridgeless boost converters. Moreover, the converter architecture enables simple implementation of lightning and surge protection systems. And it has inherent inrush current limit capability. Experimental results of a prototype unit converting a universal ac input to a 400 V dc output at 650 W and 70 kHz switching frequency are presented to verify the proof of concept.

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: none
Teacher disagreement score0.962
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 head0.006
GPT teacher head0.197
Teacher spread0.192 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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