A Rotor Flux Linkage Estimator and Operating Envelopes of a Variable-Flux IPM Synchronous Machine
Bibliographic record
Abstract
Interior permanent magnet synchronous machines (IPMSMs) with rare-earth magnets are widely used by the electric and hybrid electric vehicle industry due to their high efficiency and high torque density. The drawbacks of the IPMSMs like the fluctuating prices of the rare-earth permanent magnets (PMs), the difficulty in flux weakening, and relatively low efficiency in the high-speed region, triggered the need for alternative electrical machines for traction applications. The variable-flux type IPMSMs, also called memory motors, is a promising technology for electrified transportation applications. These machines make use of low-coercivity magnets such as AlNiCo magnets, which makes them rare-earth PM independent. Moreover, owing to the low-coercivity, the AlNiCo magnets can be demagnetized in the high-speed region. This reduces or eliminates the extra current component needed for flux weakening, which results in lower copper/iron losses and improved machine efficiency. Besides, the variable-flux IPMSMs can provide torque densities comparable to rare-earth IPMSMs in high-torque low-speed regions. \nSince the magnetization state of AlNiCo magnets can be varied online by a short stator current pulse, and the current needed for a particular magnetization state is machine parameter dependent, it is of a vital importance to the drive system to keep track of the magnet flux during transient and steady-state conditions. Moreover, failing in depicting the actual magnetization state of the magnets means a mismatch between the real value of the magnet flux in the machine and the estimated one in the controller, which directly affects the resultant torque and performance. In addition, the current pulse excitation method for magnetization causes non-uniform variable flux distribution in the air-gap. Therefore, an estimation algorithm of the rotor flux linkage of variable-flux IPMSMs via flux harmonics extraction has been proposed. Compared to the existing methods, this method does not need any voltage or current signal injection into the stator winding. The algorithm was experimentally evaluated for different magnetization states and showed a good performance in tracking the rotor flux linkage variations during transient and steady-state conditions \nThe operating envelopes of the variable-flux IPMSM were found to be affected by the nonlinearity of the magnet flux with the machine direct axis current. New analytical solutions for the operating point were reached for maximum power and maximum output voltage control for the variable-flux IPMSM taking into consideration this nonlinearity. The experimental measurement performed also support the analytical results. \nThe irreversible demagnetization of the low-coercivity magnets in the high-speed region results in extending the braking time of the variable-flux IPMSMs. A simple yet effective minimal-time braking algorithm is proposed and experimentally validated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".