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Record W4385627091 · doi:10.1109/access.2023.3302347

Wireless Power Transmission System for Vehicle Based on Multi-Transmitter Coils Array

2023· article· en· W4385627091 on OpenAlexaff
Qi Le, Ruiming Wu, J. Chen, Meiling Huang, Yang Fu, Feng Huang, Lijun Wang, Shan Du, Qipeng Li

Bibliographic record

VenueIEEE Access · 2023
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsFord Motor Company (Canada)
FundersZhejiang University of Science and TechnologyZhejiang UniversityNational Natural Science Foundation of ChinaZhejiang Gongshang University
KeywordsElectromagnetic coilTransmitterPower transmissionTransmission (telecommunications)WirelessWireless power transferInductive couplingCoupling (piping)Computer scienceElectrical engineeringPower (physics)Electronic engineeringAcousticsTelecommunicationsPhysicsEngineeringMechanical engineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Wireless power transmission technology based on coil coupling has been widely applied in various fields. However, the performance of the system is affected by the malposition of the transmission coil-couple, which is inevitable in the wireless power transmission system (WPTS). In this study, a multi-transmitter coil array is proposed to build a more uniform magnetic flux density for the WPTS, intending to enhance the performance of the WPTS in the case of coil-couple malposition. The model of the transmission system is built and different coil arrangement methods are analyzed. Then, the magnetic field distributions of the proposed WPTS with different coil arrays are compared using numerical simulations. Experiments are conducted to investigate the performances of the designed WPTS. The results show that WPTS with a seven-coil array can provide a more uniform magnetic field for the WPTS, whose relative deviation of output power is less than 28.55%. This study provides some clues for improving the performance of the WPTS through different arrangements of the transmitter coils.

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.268
Teacher spread0.242 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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