Battery Pack Sizing Method - Case Study of an Electric Motorcycle
Bibliographic record
Abstract
This paper presents a method for battery pack sizing for electric vehicles, applied to the case of an electric motorcycle. A novel way of analyzing battery pack performances in a single graphical tool is proposed. It is intended to help engineers get a broader understanding of the influence of design decisions from the early stages of the engineering process. In multi-cell battery packs, specifications such as energy, power, volume and mass are proportional to the total number of cells, while voltage, current are dependent of the series and parallel arrangement. Thus, presenting results as functions of the number of cells in series and parallel allows to compare quickly the various performance metrics of a pack. By applying the design constraints of the technical requirements as limiting functions, one can easily select a suitable solution that meets design goals, or assess the effect of design constraints. This graphical design tool could be used to size other electric energy storage devices such as lithium-capacitors, super-capacitors or battery pack of other chemistries than lithium- ion.
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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".