Development of Electric Bicycle Performance Testing Techniques and Adaptable Electric Bicycle Power System
Bibliographic record
Abstract
Battery powered electric bicycles and scooters, replacing the heavily polluting scooters with two-cycle internal combustion engines, provide zero-emission transportation for many parts of the world. Annual global sales of electric bicycles have risen from 36,000 in 1993 to over 500,000 in 1999 and to multi-millions today. To facilitate the development of new electric bicycles, a computerized electric bicycle testing facility has been created. Standardized testing cycles for quantitatively measuring the performance of electric bicycles have been developed. Testing results of three representative electric bicycles using the newly introduced electric bicycle testing methods and testing facility are presented. The development of a low-cost, fully Adaptable Electric Bicycle Power System (AEBPS) designed to be quickly adapted to a regular bicycle is also presented. The AEBPS can be attached to a regular bicycle in less than ten minutes, and removed in under five minutes. Performance of a converted bicycle using the AEBPS is evaluated and compared with representative commercial electric bicycles. The work forms the foundation for systematically evaluating different electric bicycle designs and for carrying out design optimization of electric bicycle power systems suitable to different markets and needs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".