Parameter Determination of PMSM using Coupled Electromagnetic and Thermal Model Incorporating Current Harmonics
Bibliographic record
Abstract
Motivation: Permanent magnet synchronous machines (PMSMs) are widely used for electric vehicle (EV) propulsion owing to its high performance capabilities over a wide operating range [1]. With advent in machine structure and inverter topologies, accurate parameter determination incorporating machine non-linearities and effects of time and space harmonics is of paramount significance for high-performance control and analysis. Although classical $dq -$axis modeling is widely incorporated: 1) it fails to incorporate the machine non-linearities such as magnetic saturation, cross-saturation and leakage effects; 2) the spatial harmonic contents caused by machine winding and structural configuration are not considered in inductances and flux linkage; and 3) the effects of operating temperature on parameter variation are neglected [2]; and 4) it requires information from experimental data or complex look-up tables for parameter determination. On the other hand, finite element analysis (FEA) is computationally extensive and modelling of time harmonics becomes complex. Thus, in this paper, a coupled electromagnetic and thermal model incorporating current harmonics for parameter determination is developed and validated for a fractional-slot distributed wound (FSDW) laboratory PMSM.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".