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Record W2902379992 · doi:10.23919/icems.2018.8549291

Demagnetization Analysis of Interior Permanent Magnet Machines Under Integrated Charging Operation

2018· article· en· W2902379992 on OpenAlexaff
Wenlong Li, Guodong Feng, Chunyan Lai, Ze Li, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia UniversityUniversity of Windsor
Fundersnot available
KeywordsMagnetDemagnetizing fieldElectromagnetic coilAutomotive engineeringTraction motorElectrical engineeringTraction (geology)MultiplexingElectric vehicleVoltageEngineeringMaterials scienceMechanical engineeringPhysicsMagnetic fieldPower (physics)Magnetization

Abstract

fetched live from OpenAlex

Integrated charging, multiplexing the motor windings for battery charging in electric vehicles, can improve the onboard fast charging capability without increasing the weight and cost. However, during the charging operation, the magnets in the traction motor have a risk of irreversible demagnetization caused by the charging currents. This paper investigates the demagnetization effects of charging current on magnets in interior permanent magnet machines. By using numerical analysis and circuit-coupled simulation, the demagnetization effects during integrated charging are evaluated.

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 categoriesInsufficient payload (model declined to judge)
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 score0.932
Threshold uncertainty score0.998

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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 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
Published2018
Admission routes1
Has abstractyes

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