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Record W3011472810 · doi:10.1109/wow45936.2019.9030640

A Dynamic Wireless Charging System with a Robust Output Voltage Respect to Misalignment

2019· article· en· W3011472810 on OpenAlexaff
Ali Ramezani, Mehdi Narimani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInductanceElectromagnetic coilVoltageCoupling (piping)Sensitivity (control systems)Topology (electrical circuits)Control theory (sociology)Coupling lossWirelessComputer scienceElectronic engineeringElectrical engineeringMaterials scienceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

In a dynamic wireless charging application, the mutual inductance of the secondary side coil and primary side coils is a time-dependent variable. Therefore, the induced voltage on the vehicle side coil will be changed by the variation of the mutual inductance. In this paper, the effect of the coupling factor (k) variation on the output voltage of an LCC-S compensated topology is analyzed and sensitivity function is defined. In order to reduce the effect of the variation of k on the output voltage and efficiency, an optimization problem is defined. Moreover, to achieve high efficiency for a wide range of k, desired constraints are considered in the problem. As a case study, a 500 W/85 kHz system is optimized and variation of the output voltage under different coupling factors are simulated. The effectiveness of the design is validated by experimental measurements.

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)0.0010.000
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)0.0010.001

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 head0.186
Teacher spread0.179 · 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
Published2019
Admission routes1
Has abstractyes

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