System-Level Optimization of Hybrid Excitation Synchronous Machines for a Three-Wheel Electric Vehicle
Bibliographic record
Abstract
In this article, a two-level methodology is proposed to optimize the design of a hybrid excitation synchronous machine (HESM) for a given electric vehicle (EV) over an arbitrary-selected driving cycle. We are looking at a huge analysis problem of finding an optimal hybridization ratio (HR) between the two excitation sources, namely, permanent magnet (PM) and wound excitation (WE). To find the optimal HR, the HR is scanned from 0 to 1 or from pure WE to pure PM excitation. For each HR, the motor is optimally designed at the component level, its cost is minimized, and its global efficiency over the selected driving cycle is calculated. Then, at the system level, the global efficiencies associated with each HR are compared in order to find the optimal HR. The complexity of the design optimization at the component level is addressed by nondominated sorting genetic algorithm II (NSGA-II). To make a compromise between the accuracy and speed of calculations, a nonlinear 3-D dynamic magnetic equivalent circuit (MEC) model is developed and evaluated by commercial finite element analysis (FEA) software. Following the proposed methodology and due to 300 h of computations with 48 CPU cores in parallel, the final HESM design can achieve up to 18.65% higher global efficiency than pure WE and 15.8% higher than pure PM excitation.
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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".