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Record W4296871384 · doi:10.1109/tte.2022.3209098

Integrated Multipurpose Power Electronics Interface for Electric Vehicles

2022· article· en· W4296871384 on OpenAlexafffund
Tamanwè Payarou, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPower electronicsInterface (matter)Flexibility (engineering)Vehicle-to-gridPlug and playPlug-inGridAutomotive engineeringEngineeringBenchmark (surveying)PropulsionInverterElectronicsPower (physics)Electrical engineeringComputer scienceElectric vehicleVoltage

Abstract

fetched live from OpenAlex

A novel integrated multipurpose power electronics interface (IMPEI) for the new generation of plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs) is proposed in this article. The IMPEI is a reconfigurable power electronics interface (PEI) that integrates the onboard charger with the drive inverter allowing the same interface to be used for various modes of operation such as propulsion and regenerative braking as well as vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations with grid flexibility. For each mode of operation, the IMPEI is reconfigured into a previously existing power converter topology. The principles of operation and coordination of various modes of operation of the IMPEI are explained in this article. Based on component count, operating modes, and control complexity, a comparison of the IMPEI and recently proposed integrated PEIs (IPEIs) is provided. Specifications of BMW i3 are used as the benchmark for comparing size, cost, and efficiency. This article discusses and validates the experimental results.

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 score0.788
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 integrity0.0000.001
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 head0.258
Teacher spread0.244 · 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

Citations33
Published2022
Admission routes2
Has abstractyes

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