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A New Modular Level-2 PEV Charger for Plug-in Electric Vehicle: Design and Implementation

2021· article· en· W3215883918 on OpenAlexaff
Laith Alkhawaldeh, Lingli Gong, Mohamed Z. Youssef

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTotal harmonic distortionBattery chargerModular designElectric vehicleElectrical engineeringBattery (electricity)Electric-vehicle batteryEngineeringBattery packVoltageGridInductorElectronic engineeringComputer scienceAutomotive engineeringPhysicsPower (physics)Mathematics

Abstract

fetched live from OpenAlex

This paper presents a novel design for a leve1-2 battery charger for plug-in hybrid/electric vehicles. The converter is of two stages. First, an interleaved boost converter is used to minimize the total harmonic distortion (THD). The second stage is a half-bridge LLC resonant converter used for dc-dc conversion. PSIM modelling and design methodology are detailed for a 7.6 kW charger, as a case study, charging the battery with an output voltage of 230V to 340V from 240V, 60Hz single-phase grid. Simulation results are provided to prove the compliance with the NEMA 5-15 and NEMA 6-15 standards’ requirements.

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 score0.007
Threshold uncertainty score0.023

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 science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.003

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 head0.023
GPT teacher head0.261
Teacher spread0.238 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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