Non-Conventional Concentric Winding Layout Design of Hairpin Windings for Enhanced Traction Performance of Induction Machines
Bibliographic record
Abstract
Future electric vehicle (EV) traction motors require high power density, high efficiency, wider speed range, lower torque ripple, and lower weight and volume. Hairpin (HP) windings are a favored option for traction motor windings; however, their efficiency tends to degrade due to AC losses at high operating speeds. Therefore, HP winding designs that offer higher EM performances, including higher efficiencies in the full operational region, and lower winding weight are necessary for future EVs. In this regard, this paper investigated the arrangement of the HP windings within the stator slot, considering different phases, and utilized an improved winding function-based model and analytical AC loss estimation to propose an optimal concentric winding (CW) configuration for a commercially available 140 kW, 15000 rpm induction machine (IM). According to the results, except for the similar torque capacity in the maximum torque per ampere (MTPA) region, the proposed optimal CW IM configuration demonstrated superior overall performance and characteristics, including output power, torque, and efficiency, across a wide speed range; both MTPA and field weakening regions compared to the IM with distributed windings (DW). Additionally, the proposed CW configuration reduces winding weight by 12.96% compared to the IM with conventional DW, which is a significant advantage in terms of weight and volume reduction.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".