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Record W2786928574 · doi:10.1109/epec.2017.8286140

Design of 3.3 kW wireless battery charger for electric vehicle application considering bifurcation

2017· article· en· W2786928574 on OpenAlexaff
Kunwar Aditya, Vijay K. Sood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBifurcationControl theory (sociology)MATLABComputer scienceCoupling (piping)Maximum power transfer theoremBattery (electricity)InverterPower (physics)EngineeringControl (management)Electrical engineeringPhysicsVoltageNonlinear systemMechanical engineering

Abstract

fetched live from OpenAlex

Bifurcation in a resonant inductive power transfer (RIPT) system causes hard switching of the primary side inverter, decrease in efficiency and loss of control stability. Bifurcation can be avoided by either selecting complicated control strategies or by calculating the parameters of RIPT system in such a way that the system has only one resonant frequency for the entire expected range of load and coupling variations. Many control methods to tackle the bifurcation issue have been covered in the literature. This paper aims at handling the bifurcation by proposing an analytical design procedure. A fabricated system, based on the parameters calculated using presented design steps, avoids the bifurcation phenomenon for the entire coupling and load variations. A 3.3 kW wireless charger setup using series-series compensated RIPT (SS-RIPT) system and 2-phase interleaved boost power factor correction (PFC) as a front end converter has been designed using the proposed method as an example. Simulation results using MATLAB are presented to verify the proposed design methodology.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.234
Teacher spread0.210 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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