Improved High-Frequency Voltage Injection Based Permanent Magnet Temperature Estimation for PMSM Condition Monitoring for EV Applications
Bibliographic record
Abstract
Permanent magnet (PM) temperature is critical to ensure high-performance and reliable control of permanent magnet synchronous machines (PMSMs) for electric vehicle (EV) applications. High-frequency (HF) voltage injection based approach has been shown to be capable of PM temperature estimation under all-speed range with simple implementation. This paper improves existing HF voltage injection based PM temperature estimation approach by considering the cross-coupling effect. The key to PM temperature estimation is the temperature-dependent HF resistance estimated from the injected HF voltage and the current response. It is found that the cross-coupling effect has a great influence on the HF resistance estimation. This paper firstly improves the HF voltage injection model by considering the cross-coupling effect. Then, a comparative numerical investigation is conducted to analyze the estimation errors induced by the cross-coupling effect. A novel HF resistance estimation approach is derived from the proposed improved model and the PM temperature is calculated from the HF resistance with a linear thermal model. The influence of inverter nonlinearity is also analyzed. Experimental investigations demonstrate that the proposed approach is able to improve the performance of PM temperature estimation.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".