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Record W3084290488 · doi:10.32393/csme.2020.108

Experimental Analysis of Lithium Ion Batteries for Low Earth Orbit CubeSat Applications

2020· article· en· W3084290488 on OpenAlexafffund
Riley Cook, Lukas G. Swan

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 3 · 2020
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsDalhousie University
FundersCanadian Space Agency
KeywordsCubeSatLow earth orbitLithium (medication)Orbit (dynamics)AstrobiologyIonAerospace engineeringMaterials scienceComputer sciencePhysicsEngineeringSatellite

Abstract

fetched live from OpenAlex

Three groups of 18-65 cylindrical lithium-ion cells with different positive active materials (NCA -nickel cobalt aluminium, NMC -nickel manganese cobalt, LFP -lithium iron phosphate) and electrode designs (high power, high energy) were tested using an accelerated low earth orbit (LEO) CubeSat power profile cycle. Each design yields a unique energy density, power capability and cycle life. Each cell type was tested in a 3P group configuration at 10 C under hard vacuum (~0.2 kPa) and atmospheric pressure (~101 kPa). Cells groups were operated in their respective ambient condition until they failed to successfully execute the accelerated LEO cycle. In atmospheric and vacuum pressure, both NMC groups failed due to internal resistance growth. The atmospheric pressure NCA group failed due to excessive internal gas build-up causing the CID (current interrupt device). The NCA group in vacuum pressure has completed approximately 2,680 orbits or 505 equivalent 100% SoC cycles and is still operational showing 25% capacity degradation from its initial capacity. The LFP groups both in vacuum and atmospheric pressure have completed approximately 2,680 orbits or 1,730 equivalent 100% SoC cycles and are still operational with both showing 13% capacity degradation from their initial capacity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.219
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueProgress in Canadian Mechanical Engineering. Volume 3Same topicSpacecraft Design and TechnologyFrench-language works237,207