A Novel Winding Design for EV Traction Electric Motors: Hybrid Hairpin Winding Layout Containing Both Copper and Aluminum Windings
Bibliographic record
Abstract
Emerging design considerations for future electric vehicle traction machines prioritize innovative hairpin winding designs that deliver superior electro-magnetic performance across a wider speed range while maintaining lightweight construction and cost-effectiveness. To address this challenge, incorporating aluminum as the winding material presents a viable solution due to its lightweight and cost-effective nature however, overcoming its higher ohmic losses, while meeting the higher performance targets remains a main critical obstacle to be addressed. In this respect, utilizing the analytical models: winding function-based model, and winding's AC loss estimation model, this paper proposed a novel hybrid hairpin winding design layout that combined the integer slot distributed windings (ISDW) and variable pitch concentric winding layouts for a commercially available traction machine. The hybrid winding layout incorporates both copper and aluminum windings, resulting in a reduction of 36.1% in winding weight and 46.6% in cost compared to the conventional ISDW design windings with only copper. Additionally, the hybrid winding layout exhibits higher electromagnetic performance including output power, torque, and efficiency, over wider speed range, encompassing both the maximum torque per ampere and field weakening regions
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".