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Record W2570696796 · doi:10.1109/iecon.2016.7793235

Modified resonant converters for contactless capacitive power transfer systems used in EV charging applications

2016· article· en· W2570696796 on OpenAlexaff
Deepak Rozario, Vamsi Krishna Pathipati, Akash Ram, Najath Abdul Azeez, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsConvertersWireless power transferCapacitive sensingElectrical engineeringElectronic engineeringMaximum power transfer theoremNetwork topologyWirelessCapacitive couplingPower (physics)EngineeringInductive couplingUSableComputer scienceVoltageTelecommunicationsPhysicsElectromagnetic coil

Abstract

fetched live from OpenAlex

In the past decade wireless power transfer with the help of magnetic field has been most popular. Contactless capacitive power transfer (CPT) system is an alternate form of wireless power transfer that is currently gaining popularly. However, capacitive power transfer systems are predominantly utilized in low power and small air gap applications applications due to low efficiency and high voltage that are developed across the coupling interface. This paper proposes two modified converters that utilize capacitive coupling for wireless electric vehicle charging applications. The proposed topologies over come the limitations of the existing wireless capacitive systems making them usable for high power applications. The paper discuss CPT systems for small and large airgap EV charging employment. The working, design and analysis of the two converters are detailed in this paper. The characteristics of the two converters that make the proposed topologies for EV charging applications are discussed. Finally, a 1kW CPT system is designed for the proposed converters and the simulations obtained agree well with the theoretical values that are calculated.

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 score0.006
Threshold uncertainty score0.020

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.016
GPT teacher head0.212
Teacher spread0.195 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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