Design of Unskewed Interior Permanent Magnet Traction Motor with Asymmetric Flux Barriers and Shifted Magnets for Electric Vehicles
Bibliographic record
Abstract
Interior permanent magnet synchronous motors (IPMSMs) are commonly used in electric and hybrid electric vehicles. Nissan Leaf electric vehicle (EV) uses skewed-rotor IPMSM as a traction motor. This motor is considered as a benchmark in this work. Although, skewing improves the torque quality of the motor by reducing the torque ripple, it reduces the average torque and increases the motor manufacturing complexity and cost. This article proposes improvements to the benchmark motor torque quality without skewing. The proposed motor uses the same stator winding and rotor magnet topologies of the benchmark motor with the same geometric constraints and magnet volume. Modifications are applied to the placement of the magnets in the rotor and the shape of the flux barriers to achieve the performance requirements. The design procedure of the proposed unskewed design is illustrated. Moreover, the electromagnetic performance of the proposed design is investigated. The design shows competitive performance in terms of the average torque, torque ripple, cogging torque, and efficiency compared to the benchmark motor. The mechanical integrity of the design is also verified. The proposed design is found to be a suitable alternative to the benchmark traction motor with a reduced rotor weight and without skewing.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".