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A Dynamic Capacitive Wireless Power Transfer System using Four Vertical Plates for Electric Mobility Applications

2025· article· en· W4413144997 on OpenAlexaff
S. Kodeeswaran, A. Kannabhiran, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsWireless power transferCapacitive sensingElectrical engineeringWirelessComputer scienceMaximum power transfer theoremPower (physics)TelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

The dynamic wireless charging system is proposed in this article to improve the performance of capacitive power transfer (CPT). The present work suggests a novel CPT-based road infrastructure for dynamic charging with vertical plate arrangements consisting of four plates. The four aluminum plates were used to formulate the coupling capacitances between the transmitter (ground side) and receiver (vehicle side) ends. To increase the mutual capacitance, a dielectric material was filled in the space between plates on the vehicle side, which also results in improved output power. Six coupling capacitances were formed between the plates, and they are calculated by ANSYS Maxwell simulation software. By the circuit simplification method, the six coupling capacitances were reduced to four. The mutual capacitance and self-capacitance values were found for positioning the plates at different distances, and then the ideal capacitance was identified. The compensation circuit parameters were designed using dynamic circuit modeling and its performance was evaluated in LTspice software. Keywords- Wireless power transfer, dynamic charging, capacitive power transfer, coupler, circuit design.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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