Comparative Evaluation of Data-driven Clustering Techniques for Improved EV Motor Performance
Bibliographic record
Abstract
Electric Vehicles (EVs) have emerged as a solution for reducing greenhouse gas emissions and dependence on fossil fuels. A crucial step toward enhancing EVs performance is accurately modeling and optimizing motor operation under real-world driving conditions. Torque-speed operating points are useful for understanding motor requirements under various operating conditions. Clustering techniques simplify complex drive cycle data into a concise set of representative operating conditions. Building on literature work [1], this study investigates alternative clustering algorithms beyond standard K-Means to more effectively identify meaningful motor operating zones for EV powertrain optimization for a Worldwide harmonized Light vehicles Test Cycle (WLTC). The methods examined include a Hybrid K-means–Support Vector Machine (SVM) approach, Gaussian Mixture Models (GMM), and Spectral Clustering. Using EV motor’s torque-speed data, each method’s ability to extract distinct, meaningful clusters that represent operational patterns is evaluated. Cluster centroid locations, associated efficiencies, and cluster weights are analyzed to assess each method’s suitability for optimizing EV motor performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".