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Record W4411446270 · doi:10.1109/tte.2025.3581482

Magnetically Coupled Interleaved Buck Integrated On-Board Charger for Light Electric Vehicles

2025· article· en· W4411446270 on OpenAlexafffund
Daniel Afriyie, Ashraf Ali Khan, M. Tariq Iqbal

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2025
Typearticle
Languageen
FieldComputer Science
TopicInternet of Things and Social Network Interactions
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrical engineeringOn boardAutomotive engineeringComputer sciencePhysicsEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents a novel onboard charger that integrates the electric vehicle’s motor into its charging system. One of the novelties of this invention is the use of a permanent magnet synchronous motor (PMSM) as a coupled inductor in place of a conventional inductor. Additionally, the inverter used to drive the vehicle’s motor is used in conjunction with the PMSM to establish an interleaved buck converter configuration for battery charging. This is made possible because the special built-in features of the PMSM allow the motor to remain still without any torque generation and decrease the current ripples in the charging mode of operation. The minimal current ripple content and the zero-torque generation have been proven mathematically, and the different charging operational modes of the model have been studied. The proposed charger’s efficiency has also been analyzed, tested and evaluated experimentally on a 960W charger to confirm the robustness of this novel system in this paper. The research findings prove that the system performs very well, with an efficiency of 94%, providing a reliable charging solution for light electric vehicles.

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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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