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Record W2100741143 · doi:10.1109/apec.2012.6166000

A zero voltage switching full-bridge DC-DC converter with capacitive output filter for a plug-in-hybrid electric vehicle battery charger

2012· article· en· W2100741143 on OpenAlexaff
Deepak Gautam, Fariborz Musavi, Murray Edington, Wilson Eberle, William G. Dunford

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaDelta-Q Technologies (Canada)
Fundersnot available
KeywordsRectifier (neural networks)Flyback converterElectrical engineeringBoost converterForward converterVoltageBattery chargerCapacitive sensingĆuk converterPulse-width modulationBuck–boost converterFilter (signal processing)EngineeringElectronic engineeringBattery (electricity)Computer sciencePower (physics)Physics

Abstract

fetched live from OpenAlex

In this paper, a novel zero voltage switching full-bridge converter with trailing edge pulse width modulation and capacitive output filter is presented. The target application for this work is the second stage dc-dc converter in a two stage 1.65 kW on-board charger for a plug-in hybrid electric vehicle (PHEV). For this application the design objective is to achieve high efficiency and lower cost in order to minimize the charger size, charging time and the amount and cost of electricity drawn from the utility. A detailed converter operation analysis is presented along with simulation and experimental results. In comparison to a benchmark full-bridge with LC output filter, the proposed converter reduces the reverse recovery losses in the secondary rectifier diodes, therefore enabling a converter switching frequency of 100 kHz. Experimental results are presented for a prototype unit converting 400 V from the input dc link to an output voltage range of 200 V to 450 V dc at 1650 W. The prototype achieves a peak efficiency of 95.7%.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations31
Published2012
Admission routes1
Has abstractyes

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