Hybridisation ratio for hybrid excitation synchronous motors in electric vehicles with enhanced performance
Bibliographic record
Abstract
For electric vehicles (EVs) with severe acceleration requirement, the selected motor would be inevitably overdesigned to meet the acceleration requirement. To address this, the motor constant power speed ratio (CPSR) should be increased to remove part of the overdesign. There are different flux weakening techniques that are used to increase motor maximum speed (and increase the CPSR). Among them, hybrid excitation synchronous motor (HESM) advantages have been benefited in this study. CPSR depends on hybridisation ratio (HR) of the excitation system, and the motor inductance. The relation is analytically derived in this study. In addition to increasing CPSR, HR can control the place of motor high‐efficient area over the efficiency map, which can increase EV total efficiency. A search algorithm has been developed, here, to find the optimal HR of a non‐optimal HESM. The final design gives an efficient motor performance with less overdesign in drivetrain. Compared with the original permanent magnet synchronous motor, 4.1% improvement in total efficiency for an average city‐highway driving cycle has been achieved, and 16% decrease in rated values of drivetrain elements is obtained.
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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) | 0.002 | 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".