Improved Stator Current Vector Determination Considering Harmonic Iron Loss for Maximum Efficiency Control of PMSM in EV Applications
Bibliographic record
Abstract
Accurate and comprehensive control of interior permanent magnet synchronous machine (IPMSM) over a wide speed and load range is of paramount importance for a superior performance of the traction motor and its drive in electric vehicles. Many control methods such as loss minimization and maximum efficiency considering motor and inverter controllable losses have been developed in literature to improve the efficiency of the motor - drive. Stator harmonic iron losses contribute to a significant amount of controllable electrical losses in PMSM. In this paper, a novel dq-axis based harmonic iron loss model has been initially developed to consider the harmonic iron losses due to time harmonics from a sine pulse width modulated inverter. Subsequently, the model has been used in an offline procedure towards determining optimal current advance angle for improving the efficiency of an IPMSM. The improved PMSM loss model and subsequently, the analytical efficiency models have been derived by considering the varying motor parameters. The accuracy of the developed harmonic iron loss model has been validated using numerical simulations and experimental investigations on a laboratory 4.25 kW scaled- down traction IPMSM. The effectiveness of the control method using the improved model in increasing the efficiency has also been validated experimentally.
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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".