Operating Envelopes of the Variable-Flux Machine With Positive Reluctance Torque
Bibliographic record
Abstract
Variable-flux interior permanent magnet synchronous motors (VFIPMSMs) find growing attention for electrified transportation applications, especially in the area of electric vehicles. While a prior focus was on optimizing the magnetization requirement to not oversize the inverter, and improving the machine power over a wide operating range, this paper aims to investigate and compare different possible operating envelopes of the VFIPMSM from the drive point of view. Demagnetizing the low-coercive magnets via only a short d-axis current pulse eliminates the need of continuously applying a negative d-axis current in the flux-weakening region; hence, lower copper loss and improved motor efficiency are expected. In this paper, this has been investigated and compared with the utilization of continuous negative d-axis current in the flux-weakening region considering the nonlinear demagnetization characteristics of the low-coercive magnets. The latter scheme has been seen to improve the high-speed output characteristics and to extend the speed range. Although a constant-power-speed range with VFIPMSMs is not feasible due to the irreversible demagnetization of lowcoercivity magnets, an improvement of high-speed output power is shown to be feasible via saliency manipulation with the latter scheme. A VFIPMSM with a positive-reluctance torque (inverted saliency Lqd) is used for experimental validation.
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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) | 0.002 | 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".