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Record W2995546217 · doi:10.1109/iecon.2019.8926831

A Novel Hybrid Modelling Approach Towards Comprehensive Drive Cycle Analysis of Si, SiC, and GaN based Electric Motor Drives

2019· article· en· W2995546217 on OpenAlexaff
Philip Korta, Animesh Kundu, Aiswarya Balamurali, K. Lakshmi Varaha Iyer, Gerd Schlager, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsInsulated-gate bipolar transistorHarmonicsTorque rippleInverterPowertrainPower semiconductor deviceElectric motorDrivetrainComputer scienceDriving cycleMotor driveAutomotive engineeringPower electronicsElectrical engineeringPower (physics)TorqueElectric vehicleEngineeringDirect torque controlInduction motorVoltageMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Wide-band gap (WBG) power devices are gaining significant interest over conventional Si insulated gate bipolar transistor (IGBT) power devices for inverters in electric vehicle (EV) propulsion applications. Understanding motor-drive performance on a drive-cycle due to the influence of such emerging inverters is essential to design an optimal electric powertrain system which is superior in terms of cost, weight and efficiency when compared to the state-of-the-art. Specifically, this paper introduces a novel, hybrid approach that includes numerical and analytical simulations and analysis to consider drive-cycle load and switching characteristics of various power switches and harmonics generated by the inverter. A comprehensive list of performance indices including inverter and motor losses, current harmonics and torque ripple are initially selected and used in this approach. The impact of two-level IGBT, GaN, and SiC inverters on a surface permanent magnet machine are determined for a drive-cycle.

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: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.209
Teacher spread0.193 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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