Multiobjective Optimization Design of a Switched Reluctance Motor for Low-Speed Electric Vehicles With a Taguchi–CSO Algorithm
Bibliographic record
Abstract
This paper proposes a novel multiobjective optimization design method for a switched reluctance motor (SRM) on low-speed electric vehicles (EVs). According to the indexes of a low-speed EVs propulsion system and the large torque ripple of the SRM, six objectives of geometric parameters optimization of the SRM are given, which are maximum speed, acceleration time (including in situ acceleration time and overtake acceleration time), maximum climbing gradient, energy usage ratio, and torque ripple factor. The rated parameters of the driving motor are given based on the basic parameters of the low-speed EVs. Based on the engineering design method, the dimension range of the SRM under the rated parameter range is confirmed. The dynamic simulation model of a low-speed pure EVs propulsion system is built in MATLAB/Simulink based on the finite element model of the SRM and the vehicle balance equation. Then, a multiobjective optimization design of the geometric parameters of the SRM is carried out by a Taguchi-chicken swarm optimization algorithm. The correctness of the finite element model is verified, and the accuracy of the multiobjective optimization is verified by the dynamic simulation results and the low-speed EV experiment.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".