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Record W2740484967 · doi:10.1109/itec.2017.7993356

On the concept of a novel Reconfigurable Multi-Source Inverter

2017· article· en· W2740484967 on OpenAlexafffund
Ephrem Chemali, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsPowertrainBattery (electricity)Rectifier (neural networks)VoltageInverterBattery packPower (physics)Three-phaseElectrical engineeringAutomotive engineeringComputer scienceTorqueElectric vehicleWaveformEngineeringControl theory (sociology)PhysicsControl (management)

Abstract

fetched live from OpenAlex

Nowadays, the two competing powertrains used in Hybrid and Plug-in Hybrid Electric Vehicles are embodied by the Chevrolet Volt and the Toyota Prius. These powertrain architectures are costly which is primarily due to the fact that they either use a large and expensive battery pack or a smaller battery pack coupled to a power converter. The concept of a novel Reconfigurable Multi-Source Inverter (ReMS) is introduced in this paper where two or more DC sources with variable voltages are interconnected to a three phase output load. It is capable of connecting the two DC sources in a parallel or series configuration allowing the powertrain to reduce switching losses as well as sustain peak torque for higher motor speeds, which, in turn, allows for a reduction in battery size. A modified Space Vector Pulsewidth Modulation (SVPWM) scheme is used to control the ReMS where three phase AC waveforms are synthesized from 24 non-zero voltage vectors. Simulation results of the ReMS applied in a hybrid electric powertrain are conducted where a rectifier and Li-ion battery act as the two DC sources. Modes of operation, line-to-line voltages, the sinusoidal three phase currents and the state of charge of the battery are plotted.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.240
Teacher spread0.191 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations22
Published2017
Admission routes2
Has abstractyes

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