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Record W2798955423 · doi:10.1109/ieses.2018.8349911

Efficiency analysis of a 7.7 kW inductive wireless power transfer system with parallel displacement

2018· article· en· W2798955423 on OpenAlexaff
Deepa Vincent, Soma Chakraborty, Phuoc Sang Huynh, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsWireless power transferElectromagnetic coilMaximum power transfer theoremCapacitorDisplacement (psychology)Compensation (psychology)Electrical engineeringPower (physics)WirelessElectronic engineeringFinite element methodEngineeringComputer scienceAcousticsPhysicsTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

This paper provides an efficiency model of a 7.7 kW inductive wireless charging system based on the variations in transmitting and receiving coil alignment. The design and selection of electrical and geometrical parameters for the proposed model have been outlined. The 490 mm diameter magnetic couplers were simulated at an air gap of 165 mm. The system achieved 97% coupler-coupler efficiency under ideal conditions and zero misalignment. The performance of proposed couplers were investigated for 45 mm, 90 mm, 180 mm and 250 mm horizontal displacement in 3D finite element analysis. This model operates at a switching frequency of 85 kHz and employs a single capacitor series-parallel resonant compensation to minimize the VA rating of the supply and to maximize the power transfer capability. The prototype follows the SAE J2954 specification for a level 2 wireless power transfer system.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.001

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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