Improved High-Frequency Voltage Injection Based Permanent Magnet Temperature Estimation for PMSM Condition Monitoring for EV Applications
Why this work is in the frame
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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.
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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 | 0.001 | 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 it