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Efficient Wireless Power Transfer with Self-Resonant Three-Coil Design

2025· article· W7127428977 on OpenAlexaff
Neda Zahedi Saadabad, Javad Nekoui, Qingsong Wang, Ambrish Chandra

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsWireless power transferElectromagnetic coilEfficient energy useTrack (disk drive)Power (physics)Transfer efficiencyCompensation (psychology)WirelessQuality (philosophy)

Abstract

fetched live from OpenAlex

Wireless power transfer (WPT) systems face challenges in maintaining high efficiency over long distances due to energy losses and misalignment. Multi-coil architectures, particularly three-coil configurations, provide an effective solution to enhance both transfer efficiency and range. Series compensation is widely adopted for its simplicity and ease of implementation. In addition, using self-resonant coils eliminates the need for external capacitors, further enhancing system efficiency and simplifying the design. This study proposes a seriesseries self-resonant three-coil WPT system with planar coils. Track width ratio (TWR) and track gap ratio (TGR) structures have been implemented to reduce losses and improve the quality factor of the coils. Experimental results demonstrate the system’s effectiveness, achieving maximum efficiency of $87.2 \%$ at 70 mm and $42 \%$ at a 150 mm distance between the relay and load coils, with 200 mm coil diameters and a $10 \Omega$ load.

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 score0.001
Threshold uncertainty score0.004

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.192
Teacher spread0.185 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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