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Impact of zero-voltage switching on efficiency and power transfer capability of a series-series compensated IPT system

2017· article· en· W2800362413 on OpenAlexaff
Kunwar Aditya, Sheldon S. Williamson, Vijay K. Sood

Bibliographic record

Venue2017 IEEE Transportation Electrification Conference (ITEC-India) · 2017
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMaximum power transfer theoremCapacitorResonant inverterElectromagnetic coilEngineeringElectrical engineeringRLC circuitPower factorInverterSeries (stratigraphy)VoltagePower (physics)Series and parallel circuitsElectronic engineeringPhysics

Abstract

fetched live from OpenAlex

Inductive power transfer (IPT) which transfers power from source to load through electromagnetic coupling, is being considered as a promising technology for charging of electric vehicles (EVs). IPT system is a double tuned circuit, i.e. two capacitors are added to form two resonant tanks with primary and secondary coil inductances. The secondary compensation circuit is added to improve the power transfer capability of the system and primary capacitor is so chosen to operate the inverter output at unity power factor. However, during practical implementation, switching frequency is deviated from resonant frequency to accommodate soft-switching in inverter switches. This paper deals with the impact of deviation from an ideal resonant frequency on efficiency and power transfer capability of the IPT system. In this paper, a series-series compensated IPT system has been considered for the analysis using PLECS simulation.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.249
Teacher spread0.231 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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Same venue2017 IEEE Transportation Electrification Conference (ITEC-India)Same topicWireless Power Transfer SystemsFrench-language works237,207