External-Rotor <inline-formula><tex-math notation="LaTeX">$6-10$</tex-math></inline-formula> Switched Reluctance Motor for an Electric Bicycle
Bibliographic record
Abstract
As a cost-effective, healthy, and environmental friendly personal mode of transportation, electric bicycles (E-bikes) are gaining an increasing market share from conventional bicycles and automobiles. Considering the legal rules in Ontario, Canada, a 500-W electric motor providing power assist makes the E-bike more attractive to urban commuters. The simple structure, high torque, and power density, as well as the potential for low cost make the switched reluctance machine (SRM) a strong candidate for E-bike traction. In this paper, a three-phase, external-rotor SRM with 6 stator poles and 10 rotor poles is designed for a representative E-bike. The design of an external rotor arrangement of the 6-10 SRM topology has not previously been reported, this brings the challenge of sizing the geometry of this topology, but the solution offers a new contribution to published works. The external-rotor arrangement is chosen to facilitate ease of integration into the wheel hub structure of a typical pedal bicycle. The increased rotor poles yield improved torque ripple reduction than more conventional (i.e., 6-4 and 12-8) SRM design, which is an essential feature for low-speed rider comfort. The final machine design is experimentally validated via a full system prototype and dynamometer test facility. Results highlight some limitation of the 2-D finite element analysis (FEA) study in terms of the winding inductance calculation, more accurate 3-D FEA model is implemented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.339 | 0.138 |
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".