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Record W4408017579 · doi:10.1109/tia.2025.3546174

Comparison Between a Series-Hybrid Variable-Flux Memory Motor and a Rare-Earth IPMSM

2025· article· en· W4408017579 on OpenAlexaff
Akrem Mohamed Aljehaimi, Bassam S. Abdel-Mageed, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsSeries (stratigraphy)Variable (mathematics)Control theory (sociology)Flux (metallurgy)Rare earthDirect torque controlPhysicsComputer scienceInduction motorVoltageEngineeringElectrical engineeringMaterials scienceMathematicsControl (management)Geology

Abstract

fetched live from OpenAlex

This paper compares a series-hybrid variable-flux memory motor with an equivalent optimized conventional rare-earth IPMSM for electric vehicle applications. Hybrid variable-flux motor technology with different magnetic circuit arrangements, such as series, parallel, and series-parallel, have been recently investigated for traction applications. The series-hybrid variable-flux motors (SHVFMs) have shown superiority for high torque and load demagnetization withstand capabilities. Yet, the literature lacks a detailed comparison between this evolving technology and the conventional IPMSMs for traction applications. Thus, this paper compares the two comprehensively, based on Finite Element (FE) Analysis supported by experimental results. The segregated power loss, efficiency, and power factor comparison is carried out for heavy and continuous load operation in the constant torque and field-weakening regions. It was found that for heavy load operations, the conventional IPMSM outperforms the SHVFM; however, the opposite is true for continuous load operations, significantly beyond the base speed. The magnet size/cost and inverter size are also discussed.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.963

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 integrity0.0000.001
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 head0.015
GPT teacher head0.249
Teacher spread0.235 · 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 designOther design
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
Published2025
Admission routes1
Has abstractyes

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