Design of Wave Winding with Bar Wires for Six-Phase Interior Permanent Magnet Traction Machines
Bibliographic record
Abstract
This article aims to analyze different six-phase winding configurations with rectangular bar wires to be used in an interior permanent magnet machine for an electrified vehicle application. Rectangular bar wires allow a higher slot fill factor compared to conventional round wires, and they are currently utilized in wave windings in the traction motors of commercial electrified vehicles. Combining bar wire winding with a six-phase configuration can lead to achieving a higher power density and more reliable operation in a traction motor. However, applying bar wires for a six-phase operation may raise further complications in the winding design, which requires special care to prevent complexities and potential failure points. In this article, the full-pitch wave winding pattern is discussed and then utilized for the implementation of pure six-phase and dual three-phase windings. The potential implementation process and the type of connections between different winding segments have also been considered. Finally, a comparison between the two winding arrangements has been presented in terms of complexities in the mechanical design and implementation. In order to find the optimal winding design, a MATLAB script has been provided, which enables investigating all possible winding configurations for both pure six-phase and dual three-phase windings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".