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Record W2021723122 · doi:10.1109/isie.2014.6864651

Comparative study of Series-Series and Series-Parallel compensation topologies for electric vehicle charging

2014· article· en· W2021723122 on OpenAlexaff
Kunwar Aditya, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsConstant currentSeries (stratigraphy)Electrical engineeringVoltageCurrent sourceComputer scienceCompensation (psychology)Constant (computer programming)Series and parallel circuitsElectric vehicleBattery (electricity)Maximum power transfer theoremVoltage sourceTraction (geology)Power (physics)Topology (electrical circuits)EngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Loosely coupled inductive power transfer (IPT) systems have recently gained enormous attention for electric vehicle (EV) battery charging. For EV battery charging, a constant-current source is required. Numerous published papers suggest that the secondary of loosely coupled IPT systems, if series compensated, can act as constant-voltage source; and, if parallel compensated, it can act as constant-current source. In this paper, the authors prove that both series as well as parallel compensated secondary can act as constant-current source as well as a constant-voltage source, depending on the nature of power supply. Hence, either of the topological options can be utilized efficiently for EV charging. The authors intend to present the work for the case where primary is in the form of a long track, such as in a mono-rail or electric traction metro system. Hence, the primary is always considered to be series compensated.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations60
Published2014
Admission routes1
Has abstractyes

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