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Record W4403390428 · doi:10.1109/tte.2024.3471683

Unequal-Thickness Flat Wire Winding-Based Eddy Current Loss Reduction for Coreless Axial Flux Permanent Magnet Synchronous Machine Adapted to Extended-Range Electric Vehicles

2024· article· en· W4403390428 on OpenAlexaff
Xiaoguang Wang, Hao Yin, Jian Ge, M. H. Chen, Yifan Zhou, Zhiheng Lin, Rong Yu, Wei Xu

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2024
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Alberta
FundersScience and Technology Development Fund
KeywordsEddy currentMagnetReduction (mathematics)Range (aeronautics)Permanent magnet synchronous generatorCurrent (fluid)Flux (metallurgy)Electromagnetic coilElectrical engineeringMagnetic fluxMaterials scienceMechanical engineeringEngineeringPhysicsAerospace engineeringMagnetic fieldMetallurgyGeometryMathematics

Abstract

fetched live from OpenAlex

Axial flux machines are increasingly attracting attention due to their power density and compact structure, making them particularly suitable for extended-range electric vehicles (EREVs). Among these, the coreless axial flux permanent magnet synchronous machine (CAFPMSM) with flat wire is renowned for its high copper filling, thermal conductivity, and efficiency. However, the flat wire in CAFPMSM is subject to substantial eddy current losses induced by the magnetic field. To mitigate eddy current loss, an unequal-thickness winding structure with multilayer flat wire is proposed in this article. A mathematical model is first developed to quantify the eddy current losses based on the air-gap flux density distribution. The relationship between the thickness of each layer of the winding and the resulting eddy current loss is analyzed. Second, the proposed winding configuration is further evaluated through 3-D finite element analysis (FEA) to assess its effectiveness in reducing losses. Finally, comprehensive experiments are carried out to demonstrate the effectiveness of the unequal-thickness windings in reducing eddy current loss and improving the efficiency of the 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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