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Parameter Estimation for IPMSMs Considering Eddy Current Based on Joule Heating Effects and VMC Theory from Multi-State Measurements

2025· article· W7123352859 on OpenAlexaff
Hongfu Cheng, Uday Deshpande, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsEddy currentInductanceFlux linkageControl theory (sociology)Joule (programming language)Magnetic circuitEstimation theoryElectronic circuitEquivalent circuit

Abstract

fetched live from OpenAlex

Accurate parameters are critical for efficient operation, optimizing performance, and detecting faults of the interior permanent magnet synchronous machines (IPMSMs). Eddy current is one of the key factors affecting parameter estimation accuracy, because eddy current can affect both the equivalent electric circuit and flux linkage in IPMSMs. Achieving a balance between the accuracy of the core loss model and engineering feasibility is a challenge in parameter estimation. Recently, a vector magnetic circuit (VMC) theory has been proposed to accurately analyze the influence of eddy current on the magnetic circuit of electric machine (EM), which can help develop a more precise IPMSM model. This paper proposes a parameter estimation method for improving inductance estimation accuracy considering the Joule heating effect and the impact on magnetic circuits of eddy current.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.035
GPT teacher head0.285
Teacher spread0.250 · 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
GenreMethods

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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