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Design and Analysis of Four-Plate Capacitive Couplers for Wireless Charging Systems

2022· article· en· W4361793744 on OpenAlexaff
Pramod Patidar, Deepak Ronanki, Apparao Dekka

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsLakehead University
FundersIndian Institute of Technology Roorkee
KeywordsCapacitive sensingCapacitanceMaximum power transfer theoremWireless power transferTransmitterElectrical engineeringHybrid couplerAir gap (plumbing)Capacitive couplingDielectricCoupling coefficient of resonatorsCoupling (piping)Electronic engineeringPower dividers and directional couplersPermittivityPower (physics)Materials scienceEngineeringElectromagnetic coilPhysicsVoltageMechanical engineeringResonatorChannel (broadcasting)

Abstract

fetched live from OpenAlex

Capacitive power transfer (CPT) systems have become a popular alternative to inductive power transfer (IPT) systems owing to their unique features such as better misalignment tolerance, relatively inexpensive, and lightweight. However, the main concern with CPT is the low coupling capacitance (typically pico farads (pF) range) between transmitter and receiver plates due to the natural low permittivity in air. Consequently, the power transfer capability is limited, which makes CPT systems unsuitable for high-power large air gap applications. This paper investigates a new four-plate coupler structure with a dielectric medium on the transmitter and receiver plates. The proposed design of the CPT coupler increases the coupling coefficient and improves the power density of the coupler with a better misalignment tolerance. The effectiveness of the proposed CPT coupler is validated through ANSYS-Maxwell simulations. Furthermore, its performance is compared with other configurations in terms of electric field emissions, misalignment tolerance, and coupling capacitance values at a constant excitation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.694
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.209
Teacher spread0.184 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

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