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Record W2588321229 · doi:10.1109/ecce.2016.7855395

Research on seamless transfer from CC to CV modes for IPT EV charging system based on double-sided LCC compensation network

2016· article· en· W2588321229 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsMaximum power transfer theoremInductanceTransformerLeakage inductanceCompensation (psychology)VoltageTransmission networkElectrical engineeringElectric power transmissionConstant voltageComputer sciencePower (physics)Electronic engineeringTransmission (telecommunications)EngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, based on a double-sided LCC compensation network, the leakage inductance equivalent model of the loosely coupled transformer (LCT) is built and the constant current (CC) output with load independent is analyzed in an inductive power transfer (IPT) system. This paper proposes four resonance conditions for achieving constant voltage (CV) output in various load conditions for the compensation network. The Zero Voltage Switching (ZVS) operation for the primary side H-bridge converter can be achieved in CC mode and CV mode to minimize the switching loss. A seamless transfer control strategy is also investigated to realize transmission from CC mode to CV mode for IPT EV (electric vehicle) charging system. An IPT EV system with 3.3 kW transmission power is built. The theoretical analyses are confirmed by simulation and experiment results.

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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.072
GPT teacher head0.297
Teacher spread0.226 · 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

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

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