Development of an Electric Propulsion System Sizing Framework Considering Battery Degradation.
Bibliographic record
Abstract
With the global shift towards low-emission technologies, electric propulsion systems (EPS) with lithium-ion (Li-ion) batteries have been identified as a possible candidate for the decarbonization of small aircraft on short missions. This paper develops an EPS sizing framework for electric aircraft with a focus on battery lifespan using publicly available data. The battery sizing is done in three parts: an initial system sizing, an iterative sizing through a worst-case mission simulation, and lifespan degradation modelling through a cycling simulation. The sizing for the other EPS components are completed based on existing components. Two test cases are used to evaluate the framework: a single-engine trainer aircraft, and a distributed propulsion, vectored-thrust eVTOL aircraft. The results from the framework can inform aircraft designers of the expected lifespan of the batteries and how the aircraft operation can affect it in the early stages of the design. With these results, the overall change in environmental and economic feasibility of the aircraft can be more accurately determined as the frequency or necessity of replacing battery packs is estimated.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".