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Record W3211338056 · doi:10.1109/tie.2021.3127032

Integrated Transformerless EV Charger With Symmetrical Modulation

2021· article· en· W3211338056 on OpenAlexaff
Caniggia Viana, Sepehr Semsar, Mehanathan Pathmanathan, Peter W. Lehn

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInverterModulation (music)Leakage (economics)Electronic engineeringElectrical engineeringCommon-mode signalPower (physics)Computer scienceVoltageEngineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

This article introduces a modulation technique that nearly eliminates the common-mode (CM) leakage currents in the transformerless (nonisolated) dual-inverter single-phase charger. This improvement allows for the safe use and commercialization of the transformerless charger, making superior charger efficiency, power density, and cost possible. The CM model of the system is derived to guide the choice of the modulation scheme. Simulations are conducted to validate the CM performance predictions derived analytically. Finally, an experimental 6.6-kW charger prototype is presented to verify the CM leakage current elimination. The experimental results show not only that the proposed modulation meets the required standards regarding CM currents, but also that it does so by a wide margin, and it even outperforms the isolated system used for comparison.

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 head0.020
GPT teacher head0.213
Teacher spread0.193 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

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