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Record W4414140465 · doi:10.4271/14-14-03-0018

Evaluation of Switching Effect on Acoustic Noise of an Interior Permanent Magnet Synchronous Motor for a Traction Application

2025· article· en· W4414140465 on OpenAlexaff
Francisco Juarez-Leon, Ashish Kumar Sahu, Reemon Z. Haddad, Dhafar Al-Ani, Berker Bilgin

Bibliographic record

VenueSAE International Journal of Electrified Vehicles · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPropulsionNoise, vibration, and harshnessVibrationNoise (video)Traction motorElectric vehicleSynchronous motorTraction (geology)Noise control

Abstract

fetched live from OpenAlex

<div>In recent years, the automotive industry has shown growing interest in the vibroacoustic characteristics of electric propulsion motors. Investigation of such characteristics can open avenues for motor design optimization and refined control strategies to mitigate vibration and acoustic noise in an electric motor. This article presents a comprehensive vibroacoustic analysis of a propulsion interior permanent magnet synchronous motor (IPMSM) under various current excitations generated by the power converter in combination with three different modulation schemes. To evaluate the switching effect from the inverter drive on motor noise, different simulations and processes are performed in ANSYS Workbench and MATLAB/Simulink. The multi-physics noise and vibration workflow, and sampling requirements used for the study are also presented. The simulation results, presented as equivalent radiated power (ERP) waterfall diagrams, show diverse acoustic noise signatures for the different types of excitation currents.</div>

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.281
Teacher spread0.273 · 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 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
Published2025
Admission routes1
Has abstractyes

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