Torque Pulsations in Variable-Flux Memory Motors
Bibliographic record
Abstract
This paper investigates the torque pulsations issue during magnetization in variable flux memory motors for traction applications. The paper proposes an algorithm to mitigate these torque pulsations and their resultant speed fluctuations. In normal saliency (Lp> Ld) variable-flux motors, and during demagnetization events, the reluctance torque is positive. This aids the magnet torque, causing an increase in motor speed. In contrast, during remagnetization events, the reluctance torque is negative, which results in an abrupt decrease in torque and motor speed. In this instance, the speed controller increases the q-axis current command to increase the speed to its reference value. This further decreases the motor torque during the pulse time. During remagnetization events, there are two instances where the developed electromagnetic torque goes to zero. This is because, at these two moments, the rotor flux linkage and the stator flux linkage are equal and opposite to each other. A control algorithm based on a q-axis current adjustment is proposed to mitigate the torque pulsations during magnetization processes. The torque pulsation issue and the proposed mitigation algorithm are illustrated via simulation and validated experimentally on a seven-horsepower series-hybrid variable-flux memory motor.
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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 | 0.001 | 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".