Torque Ripple Reduction for Interior Permanent Magnet Synchronous Machines under Load Excitation by Optimizing Rotor Skew Angles
Bibliographic record
Abstract
This paper presents a skew angle optimization method to reduce torque ripples for an interior permanent magnet synchronous machine (IPMSM) under load excitation. Single- and multi-objective optimizations are employed to maximize the output torque and/or minimize the torque ripple for an IPMSM with four-step linear rotor skew. The motor model is based on look-up tables. The rotor skew angle vector is optimized for priority operating points in both constant-torque and constant-power regions of the torque-speed map. It is found that the output torque and torque ripple can be balanced to meet the targets of the output torque quality and the voltage constraint. Both two- and three-dimensional finite element analyses are used to verify the optimized results.
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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 | 0.000 | 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) | 0.001 | 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 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".