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Proposing an External Rotor In-Hub PM Assisted Synchronous Reluctance Motor for an E-Bike

2023· article· en· W4391807851 on OpenAlexaff
Reza Nasiri‐Zarandi, Ahmadreza Karami-Shahnani, Mohammad Sedigh Toulabi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTorque rippleMagnetic reluctanceTorqueStatorTorque densityAutomotive engineeringRotor (electric)Reluctance motorSwitched reluctance motorFinite element methodDirect torque controlMagnetElectric motorSynchronous motorComputer scienceEngineeringControl theory (sociology)Mechanical engineeringInduction motorElectrical engineeringVoltagePhysicsStructural engineering

Abstract

fetched live from OpenAlex

In recent years, permanent magnet (PM) motors have commercialized light electric vehicles. Due to the high cost of rare-earth PMs and environmental side effects, the structures without or with the low number of PMs that maintain the application constraints can be a good choice. The synchronous reluctance motors (SynRM) can be a proper candidate for this application. Choosing a good value for the number of flux barriers and their end position can reduce the torque ripple. To, assist the synchronous reluctance motor in improving its capabilities, especially torque density and power factor, low number of PMs can be inset in the flux barriers. This paper proposes designing and optimizing an external rotor permanent magnet in-wheel PM-assisted SynRM (PMaSynRM) for an electric bicycle application. The rotor two-barriers and three-barriers structures with different PMs, i.e., rare-earth and Ferrite and without PM (SynRM) are comprehensively investigated by finite element analysis (FEA) in both skewed and non-skewed stator in terms of average torque and torque ripple. Finally, the design concepts are validated with a prototype machine.

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 score0.973
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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