Cogging Torque Computation in an Asymmetrical Interior Permanent Magnet Machine for Electric Vehicles
Bibliographic record
Abstract
This article computes the cogging torque in an asymmetrical 36-slot 4-pole interior permanent magnet (IPM) machine designed for high torque density and transportation applications. Cogging torque causes acoustic noise and vibration. Therefore, it is important to know the value of cogging torque in the asymmetrical IPM machine. The cogging torque of the asymmetrical permanent magnet machine is computed based on a Fourier series expansion of air gap flux density in an equivalent slot-less IPM machine and relative air gap permeance function. The flux distribution of the asymmetrical IPM machine is computed using an equivalent lumped magnetic circuit based on flux distribution obtained using the finite-element analysis (FEA) method. The computed flux distribution follows the FEA results and thus the lumped magnetic circuit is validated. Then, the cogging torque of the asymmetrical IPM machine is derived. The Fourier coefficients of the flux distribution and relative air gap permeance in the asymmetrical IPM machine are analyzed and used to compute the cogging torque and compared to the FEA results. The computed cogging torque follows the FEA results and thus the newly derived cogging torque is justified by FEA and measurement. The FFT of the cogging torque is analyzed. Skewing technique is used to minimize cogging torque.
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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 | 0.001 |
| 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 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".