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Record W4405046226 · doi:10.1587/transfun.2024eap1089

Analysis of Series-Connected Double-Layer Coils for MHz Inductive Power Transfer

2024· article· en· W4405046226 on OpenAlexaff
Quang‐Thang Duong, Kohei MATSUKAWA, Quoc-Trinh Vo, Minoru Okada

Bibliographic record

VenueIEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersJapan Society for the Promotion of Science
KeywordsSeries (stratigraphy)Maximum power transfer theoremElectrical engineeringTransfer (computing)Power (physics)Layer (electronics)TelecommunicationsComputer scienceMaterials sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

Double-layer coils (DLCs) have been extensively investigated for compact inductive power transfer (IPT) systems operating in the MHz frequency range. Different from previous studies which focus mainly on realizing self-resonance and enhancing the Q factor, this paper enhances both the Q factor and the self inductance to achieve high efficiency and guarantee target output voltage when deployed in IPT applications. By using a lumped-element model derived from transmission line concept, this paper shows that the self inductance when the two layers are serially connected is approximately more than 3 times of that when the two layers are open-ended. Motivated by this feature, we focus on the series-connected DLC and investigate a resonance scheme using two external capacitors: one inserted between the two layers and the other inserted outside the coil. In this resonance scheme, parameters of the capacitors are chosen not only to enhance the Q factor but also to maintain the self inductance. Our air-cored sample coils of 100 mm outer diameter exhibit self inductance of 7.69 μH, Q of 308 at 6.78 MHz, and 94% power transfer efficiency at 50 mm transmission distance. These results are attractive when compared to recent self-resonant open-ended DLCs having similar dimensions.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.281
Teacher spread0.249 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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Same venueIEICE Transactions on Fundamentals of Electronics Communications and Computer SciencesSame topicWireless Power Transfer SystemsFrench-language works237,207