Bidirectional electromagnetic–thermal coupling analysis for permanent magnet traction motors under complex operating conditions
Bibliographic record
Abstract
Temperature increase has a significant effect on the performance and service life of permanent magnet in-wheel motors (PMIWMs) in the traction systems of electric vehicles under complex operating conditions. Herein, we propose a bidirectional electromagnetic–thermal coupling method for analyzing the electromagnetic loss and thermal characteristics of a PMIWM considering the effect of increased temperature on the permanent magnet. The heat dissipation coefficient and electromagnetic–thermal coupling field model of each component of the PMIWM were analyzed. The distributions of electromagnetic loss and thermal loss of the PMIWM were investigated under constant-speed plus constant-torque and variable-speed plus variable-torque conditions. An 8 kW outer rotor PMIWM was used to study the electromagnetic–thermal coupling characteristics. Simulations and experimental results showed that the thermal field of each component of the PMIWM calculated using the proposed bidirectional electromagnetic–thermal coupling method was more accurate than that of the traditional unidirectional electromagnetic–thermal coupling method under complex operating conditions. The effectiveness of the proposed bidirectional electromagnetic–thermal coupling method provides solid support for the cooling design of PMIWMs operating in harsh environments.
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 | 0.001 | 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".