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Record W4408182446 · doi:10.1109/tte.2025.3548608

A New Control Method for an On-Board Charger for Electric Vehicles Operating in a Split-Phase System

2025· article· en· W4408182446 on OpenAlexaboutno aff
Tat-Thang Le, Abraham Gebregergis

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPhase controlPhase (matter)Electric vehicleControl (management)On boardElectrical engineeringComputer scienceAutomotive engineeringEngineeringPhysicsAerospace engineeringVoltage

Abstract

fetched live from OpenAlex

This article introduces a novel control method for an on-board charger (OBC) operating within a split-phase system, which is commonly used in the North American Free Trade Agreement (NAFTA) region, including the United States, Canada, and Mexico. Previous research has not extensively explored control methods for split-phase systems. In response, this article proposes a discontinuous pulsewidth modulation-minimum (DPWM-MIN) approach, which significantly improves efficiency compared to the conventional sinusoidal pulsewidth modulation (SPWM) method. The proposed DPWM-MIN method demonstrates the ability to manage grid conditions when unbalanced loads are present. A comprehensive comparison between SPWM and DPWM-MIN is conducted, focusing on current ripple, switching loss, and core loss analysis. Experimental results from a 7.68-kW OBC prototype are presented to validate the effectiveness of the proposed method. The new control method achieves a peak efficiency of 98%, which is 1% higher than the conventional method. Other performance metrics, such as total harmonic distortion (THD) and the ability to handle unbalanced currents, remain similar to those of the conventional approach.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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