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Record W2740114034 · doi:10.1109/itec.2017.7993276

Feasibility study of hybrid inductive and capacitive wireless power transfer for future transportation

2017· article· en· W2740114034 on OpenAlexaff
Deepa Vincent, Phuoc Huynh Sang, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsWireless power transferMaximum power transfer theoremCapacitive sensingWirelessElectrical engineeringPower (physics)Inductive chargingComputer scienceElectronic engineeringTopology (electrical circuits)EngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Due to the inconvenience and safety issues caused by exposed plugs and damaged cables, wireless charging of electric vehicles (EV) has gained popularity. Inductive wireless power transfer has been successfully applied up to 30kW involving the charging of EV batteries. The capacitive wireless power transfer system is new for the EV charging application, but can address the transfer of power across metallic barriers with much less losses. The performance of different compensation topology for each system is compared based on the power transfer ability, complexity, switching frequency, electromagnetic interference tolerance and efficiency. Finally, the possibility of a hybrid topology for improving the power transfer, efficiency and misalignment tolerance is considered from the literature, which can also make the system size more compact by achieving cross resonance between inductive coils and capacitive plates. The review provides a brief account of the recent laboratory prototypes for inductive wireless power transfer (IWPT), capacitive wireless power transfer (CWPT) and hybrid wireless power transfer techniques which can be adopted for wireless EV charging.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.247
Teacher spread0.228 · 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

Citations32
Published2017
Admission routes1
Has abstractyes

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