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Record W4367016794 · doi:10.1109/jestie.2023.3270107

Single-Phase Bridgeless Converter for On-Board EV Charger With Flexible Charging Capabilities

2023· article· en· W4367016794 on OpenAlexaff
Sukanya Dutta, Akshay Kumar Rathore, Vinod Khadkikar

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Industrial Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsĆuk converterBattery chargerElectric vehicleState of chargeElectrical engineeringBattery (electricity)Computer scienceEngineeringPower (physics)Electronic engineeringTopology (electrical circuits)Boost converterVoltagePhysics

Abstract

fetched live from OpenAlex

This article puts forward the analysis and design of single-phase bridgeless Cuk-derived dc–dc converter for a novel vehicle-to-vehicle (V2V) charge transfer technique. V2V charging adds flexibility to electric vehicle (EV) charging process and aids in mitigating range anxiety. The proposed V2V charge transfer technique has only one power conversion stage and only one onboard charger is used to charge the battery. The charger is capable of both grid-to-vehicle and V2V charging. The converter design is in discontinuous conduction mode which reduces the control complexity. The converter has a lower component count rendering the charger lightweight, power dense, and cost effective. The overall converter efficiency is high owing to low switching losses. This work has been compared to several state-of-the-art V2V topologies and configurations. The complete steady-state analysis and converter design operating in dc charging mode have been presented. Moreover, to validate the analysis, design, and applicability of the converter for dc V2V charge transfer, PSIM simulation, and laboratory-based hardware prototype have been built and the experimental results are provided.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.050
GPT teacher head0.302
Teacher spread0.252 · 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.

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

Citations18
Published2023
Admission routes1
Has abstractyes

Explore more

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