Demagnetization Risk Mitigation for a Traction IPMSM Using Mixed-Magnet Configurations
Bibliographic record
Abstract
The instability of prices, dominance of the market by monopolies, and limited availability of rare-earth (RE) permanent magnets have led North American companies to seek cheap reliable alternatives for electrical motors in traction applications. Consequently, the exploration of low-cost, high-performance RE-free magnets has emerged as a notable trend in current research. This paper explores the performance of RE magnets in electrical machine structure compared to lower-cost, RE-free magnets. Results reveal that while RE magnets such as neodymium iron boron (NdFeB) excel in back-EMF and torque RE-free magnets such as ferrites are more susceptible to demagnetization under extreme temperatures. To address this issue, the concept of substituting demagnetization-prone areas with stronger RE magnets such as NdFeB and samarium iron nitride (SmFeN) is presented in the paper. This strategy reduces RE material usage while upholding motor performance and mitigating demagnetization risk. Finite element analysis (FEA) demonstrates that mixed or hybrid magnet configurations strike a balance between minimizing RE content, sustaining acceptable output, and enhancing motor stability.
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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 | 0.000 |
| 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) | 0.001 | 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".