Torque ripple modeling and minimization for PMSM drives with consideration of magnet temperature variation
Bibliographic record
Abstract
The spatial harmonic of magnet flux is a major cause of torque ripple in permanent magnet synchronous machines (PMSMs), and it is temperature-dependent. Thus, this paper investigates torque ripple modeling and minimization for PMSMs considering magnet temperature variations. Firstly, experimental studies are conducted to demonstrate that the torque ripple is magnet temperature dependent. Then, based on extensive experimental tests, a novel linear model is proposed to model the relationship between the dc and harmonic components of magnet flux in the dq reference frame, which provides a way to estimate the magnet flux harmonic. Based on this model, the torque ripple model considering magnet temperature variation is proposed and validated with simulations. Afterwards, a novel adaptive current optimization approach is proposed for torque ripple minimization, which consists of two parts: the magnet flux harmonics estimation using the proposed linear magnet flux model, and the current optimization using the proposed torque ripple model. In our approach, the stator current is adaptively optimized with respect to the magnet temperature. However, the proposed approach is not necessary to run in real-time, because temperature variation has a large time constant. Our approach is validated through both numerical and experimental studies.
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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) | 0.000 | 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".