Impact of Test Conditions While Screening Lithium-Ion Batteries for Capacity Degradation in Low Earth Orbit CubeSat Space Applications
Bibliographic record
Abstract
A wide variety of commercial cylindrical lithium-ion batteries are available for use in nanosatellites (CubeSats) that cycle in low Earth orbit (LEO). This space application differs greatly from the conditions used to create the manufacturer datasheets that CubeSat teams rely on to screen cell types and estimate performance lifetimes. To address this, we experimentally test three LIB cell types using a representative LEO CubeSat power profile in three progressively complex test representations of LEO. The first is “standardized” condition (101 kPa-abs, 20 °C), which uses only a power cycler; the second adds a thermal chamber for “low temperature” condition (101 kPa-abs, 10 °C); and the third adds a vacuum chamber for “LEO” condition (0.2 kPa-abs, 10 °C). Results indicate that general “standardized” and “low temperature” conditions do not yield representative results to what would occur in LEO. Coincidentally, the “LEO” condition gives similar capacity degradation results as manufacturer datasheets. This was an unexpected finding, but suggests that CubeSat teams use full experimental thermal-vacuum testing or default to the manufacturer datasheet performance estimates during the lithium-ion cell screening and selection process. The use of a partial representation of the LEO condition is not recommended.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".