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Record W4412353294 · doi:10.1109/tie.2025.3581266

Vertical Bifilar Self-Resonant Coil for Electric Vehicle Wireless Power Transfer

2025· article· en· W4412353294 on OpenAlexafffund
S. Nie, Harpreet Singh Grover, Peter W. Lehn

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2025
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBifilar coilWireless power transferElectromagnetic coilResonant inductive couplingElectrical engineeringElectric vehicleWirelessMaximum power transfer theoremPower (physics)Materials scienceEngineeringPhysicsTelecommunicationsEnergy transferRogowski coilEngineering physics

Abstract

fetched live from OpenAlex

This article proposes a vertical bifilar self-resonant coil to replace the Litz wire and external compensation capacitors in the transmitter of a wireless electric vehicle (EV) charging system. Unlike traditional planar self-resonant coils, the vertical coil aligns with the magnetic field orientation, which reduces the impact from eddy and proximity effects, thereby reducing the ac copper loss. The vertical structure yields a high capacitance area, effectively lowering the resonant frequency. This makes self-resonance at 85 kHz viable, as required for EV charging. Meanwhile, the dielectric layer can be thicker than in planar coils due to the larger capacitance area, providing the high ac dielectric strength required for high-power charging. The vertical bifilar self-resonant transmitter coil is optimized through design procedures to minimize copper loss while meeting constraints such as resonant frequency, current density, and layer–layer voltage stress. The proposed coil structure is validated in a 3.3 kW EV wireless charging system, achieving a minimum dc efficiency of 92.47% at a lateral receiver misalignment of 150 mm.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.221
Teacher spread0.208 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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