Noninvasive and Improved Torque and Efficiency Calculation Toward Current Advance Angle Determination for Maximum Efficiency Control of PMSM
Bibliographic record
Abstract
This article proposes improved mathematical models for torque and system efficiency used toward obtaining accurate current advance angle for maximizing the efficiency of an interior permanent magnet (IPM) synchronous motor. First, improved torque and efficiency calculation procedure that consider the effects of parameter variations, such as inductance, stator resistance and PM flux linkage simultaneously, and motor and inverter losses, have been developed from a combination of analytical models and practical experiments. Subsequently, an offline search procedure has been utilized to determine the optimal current angle using the improved dq-axis-based models. The novelty of the efficiency model is that the method uses preliminary noninvasive experimental tests to consider the saturation and temperature effects simultaneously and successfully determine the relationship between stator and rotor temperatures by using only controller command voltages and currents. Experimental investigations are performed on a laboratory IPM for validating the developed control method through interpolation of improved look-up tables with the derived current angle values for varying speed, torque, and temperature conditions. The effectiveness of the proposed method in improving efficiency is also verified and compared with maximum efficiency and maximum torque per ampere methods using experimental sweep tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".