Improved Harmonic Iron Loss and Stator Current Vector Determination for Maximum Efficiency Control of PMSM in EV Applications
Bibliographic record
Abstract
The accurate control of interior permanent magnet synchronous machine (IPMSM) and drive in electric vehicle applications is vital for achieving superior performance over a wide range of speeds and loads. Many control methods such as loss minimization and maximum efficiency (ME) have been developed to improve the efficiency of the motor-drive, by mainly considering the controllable fundamental losses. This article includes the effect of stator harmonic iron losses caused primarily by inverter sideband time harmonics that contribute to a significant amount of controllable electrical losses in IPMSMs. The sideband harmonic iron losses have been analytically modeled using a novel dq-axis model incorporating harmonic iron loss resistance. Subsequently, the harmonic iron losses have been included in an offline procedure used to determine optimal current advance angle for increased motor efficiency. The improved IPMSM losses and subsequently, the analytical efficiency models have been derived by considering varying motor parameters due to saturation and cross-saturation effects. The accuracy of the developed model and the improved ME control using the optimal current angle have been validated using numerical simulations and experimental investigations on a laboratory IPMSM.
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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) | 0.001 | 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 | 0.001 |
| 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".