Comprehensive Drive-Cycle-Based Analysis of Hairpin vs Stranded Windings for EV
Bibliographic record
Abstract
Winding analysis is crucial for enhancing electric vehicle (EV) performance as it directly impacts motor efficiency, thermal management, and torque production. By examining different winding technologies, this study aims to identify optimal designs that improve overall motor performance and reliability. In this paper, a comprehensive analysis of hairpin and stranded windings for highway and urban drive cycles is conducted for two interior permanent magnet synchronous motors rated at 150 kW for EV. By integrating two distinct drive cycles, the research aims to provide a detailed assessment of efficiency, losses, and torque characteristics for each winding type. Additionally, the machines are evaluated at critical operational points, considering peak currents, and torque under the specified drive cycles. This drive cycle-based analysis provides a holistic view of the performance, reliability, and efficiency of hairpin and stranded windings, facilitating better design decisions.
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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 | 0.000 |
| 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) | 0.000 | 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".