Torque Performance Enhancement in Consequent Pole PMSM Based on Magnet Pole Shape Optimization for Direct-Drive EV
Bibliographic record
Abstract
Developing a permanent magnet synchronous machine (PMSM) for direct-drive electric vehicle (EV) has challenges such as obtaining high torque density and low torque ripple. The PMSM should have high pole numbers owing to low-speed operation, thereby increasing the use of rare earth magnets and cost. Therefore, in this article a consequent pole (CP) rotor topology is proposed in which the permanent magnet (PM) volume is reduced when compared with conventional surface PMSM (SPMSM). However, replacing south poles in an SPMSM with induced steel poles can increase torque ripple and reduce torque density. In order to improve torque density in a CP PMSM, structural modifications such as multilayer windings and non-ferromagnetic barriers have been proposed in the literature. These modifications increased the torque density while increasing the torque ripple. Therefore, this article proposes a novel two-level optimization method based on gradient descent algorithm, to address the challenges of improving torque density and reducing torque ripple simultaneously in a CP PMSM. Initially, an expression for the magnet pole arc angle is derived for CP PMSM based on magnetic equivalent circuit. A two-level optimization is performed on a baseline CP PMSM to determine the optimal magnet pole arc. The torque production and torque ripples of the optimized design are validated by simulation and experimental results.
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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) | 0.001 | 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".