Multi-Objective Robust Optimization of a Low-Cost and Efficient IPMSM for Battery Electric Vehicle
Bibliographic record
Abstract
This paper proposes a surrogate-assisted robust multi-objective optimization (MOO) approach for interior permanent magnet synchronous machines (IPMSMs). Traditional MOO methods often overlook mechanical constraints and do not focus on preserving solution diversity within the optimization process, resulting in impractical designs. By integrating robustness and diversity preservation into an effective MOO process, dimensional uncertainties in manufacturing may be managed and diversity among solutions can be maintained. This study optimizes a 165 kW peak power IPMSM found in a battery electric vehicle (BEV), balancing permanent magnet (PM) material cost and average efficiency using a robust non-dominated sorting genetic algorithm III (NSGA-III) for the preservation of diversity among Pareto-optimal solutions. Results demonstrate that incorporating robust optimization metrics into the MOO process can reduce manufacturing costs while preserving the intended machine performance, offering a promising approach for future traction motor design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".