A novel hybrid approach towards drive-cycle based design and optimization of a fractional slot concentrated winding SPMSM for BEVs
Bibliographic record
Abstract
Research conducted previously has shown that a battery electric vehicle (BEV) motor design incorporating drive-cycle optimization can lead to achievement of a higher torque density motor that consumes less energy over the drive-cycle in comparison to a conventionally designed motor. Such a motor indirectly extends the driving range of the BEV. Firstly, in this paper, a baseline fractional slot concentrated winding (FSCW) surface permanent magnet synchronous machine (SPMSM) designed for a direct-drive BEV utilizing the conventional machine design approach has been developed. A vehicle dynamics model for the baseline machine and its associated vehicle parameters are used against an urban dynamometer driving schedule (UDDS) to derive loading data in terms of torque, speed, and energy. Energy Center of Gravity (ECG) and K-means clustering are two existing methods for reducing the number of machine operating points of the drive-cycle while preserving the characteristics of the entire cycle are implemented, which offer high computational efficiency and low computational time cost while optimizing an electric machine. Understanding the merits and demerits of the two existing methods, a novel hybrid approach of drive-cycle data representation is proposed. The drive-cycle data elicited from all of the approaches are thereafter used towards optimization of FSCW SPMSMs. Finally, a comparative performance analysis of optimally designed SPMSMs using the two existing approaches and the proposed approach is conducted.
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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 | 0.001 | 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".