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Record W4416429264 · doi:10.1109/jestpe.2025.3635526

A Single-Phase Integrated On-Board Charger With Minimal Current Ripple for Electric Vehicles Having at Least One Motor

2025· article· W4416429264 on OpenAlexafffund
Daniel Afriyie, Gerald Brakoh, Ashraf Ali Khan, M. Tariq Iqbal

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2025
Typearticle
Language
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBattery chargerConvertersInductorElectric vehiclePower (physics)Battery (electricity)Controller (irrigation)PropulsionInverterTorque ripple

Abstract

fetched live from OpenAlex

This paper presents a novel and improved integrated EV charger topology that combines motor control and battery charging into a single circuit to simplify the system’s architecture. Unlike the traditional EV chargers that use interleaved boost converters and inductors to perform the charging functionalities, this new approach uses the vehicle’s motor as a coupled inductor. In addition, the inverter used to control the motor during propulsion is also used as an interleaved power factor correction (PFC) boost converter to charge the EV battery. With little to no hardware modifications, dual functionality is made possible by the motor's built-in capabilities, which ensure optimal power flow during the battery charging stage. This paper thoroughly examines the modeling and operational modes of the proposed charger. Mathematical analysis and simulations are also done on the motor to ensure the charger generates zero torque during charging mode. Furthermore, the performance of the proposed charger is evaluated, a controller is developed, and detailed simulation and experimental results are provided on a 3-kW charger. The research findings proved that the proposed charger is robust with a 93.9% experimental efficiency and a THD of 2.23%.

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 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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

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