Magnetic modeling of radial-flux and axial-flux permanent-magnet motors for direct drive automotive. Specifications and comparison
Bibliographic record
Abstract
RÉSUMÉLe présent sujet étudie la faisabilité électromécanique et technico-industrielle d’une nouvelle conception de machine à flux axial dans le but de pouvoir se positionner sur le marché concurrentiel de la traction automobile légère. L’originalité de cette nouvelle motorisation est due à la conception de son stator en « U » et de son rotor à aimants étagés ainsi que de sa connectique d’alimentation des différents bobineaux. Afin de démontrer ses performances électromécaniques, cette nouvelle machine à flux axial est comparée à une motorisation synchrone classique à aimants permanents montés en surface faisant office de référence, ayant le même diamètre extérieur et la même polarité rotorique. La modélisation électromagnétique de chaque machine est réalisée grâce à un modèle numérique par éléments finis en 3D et optimisée par logiciel utilisant les plans d’expériences. Un comparatif détaillé des résultats est présenté. Ce dernier permet de conclure que la motorisation à flux axial nouvellement conçue augure de perspectives industrielles intéressantes.ABSTRACTThis paper presents an innovative motorization, i.e., an axial-flux permanentmagnet (PM) motor (AFPMM), for direct drive automotive, mainly for electric vehicles (EVs). The AFPMM has been designed from a three-dimensional (3-D) finite-element analysis (FEA). To demonstrate these electromechanical performances, this motorization has been compared to a radial-flux PM motor (RFPMM) having the same outer diameter and the same rotor polarity. The RFPMM has been simulated by using a two-dimensional (2-D) FEA. The optimization procedure is based on a multi-parametric approach with FEA. Comparison of the results between the two electrical machines has been presented supporting the conclusion that this AFPMM will provide interesting industrial perspectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".