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Optimal sizing of a passive hybridization fuel cell – battery

2021· article· en· W4211205009 on OpenAlexaff
Thomas Jarry, Fabien Lacressonnière, Amine Jaafar, Christophe Turpin, Marion Scohy

Bibliographic record

Venue2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) · 2021
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSafran Electronics (Canada)
FundersBpifrance
KeywordsSizingBattery (electricity)Proton exchange membrane fuel cellComputer scienceAutomotive engineeringFuel cellsDual (grammatical number)Electrical engineeringEngineeringChemistryPower (physics)Chemical engineeringPhysics

Abstract

fetched live from OpenAlex

In order to size the elements of a storage system for an aeronautical application, a simplified model of a High Temperature Proton Exchange Membrane Fuel cell (HT-PEMFC) - Battery passive hybridization has been developed. It is compared to a dynamic model for validation. Sizing, done by a combinatorial algorithm, must meet a dual objective: on the one hand, satisfy the mission profile, and on the other hand, minimize the mass of the system. Several battery technologies, NiCd or lithium-ion, are studied in order to analyze the advantages and disadvantages of each in such hybridization. The characteristics of the battery appear to be decisive in the sizing of the system. The fuel cell preheating, provided by the battery, also constrains its size.

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 score0.722
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.195
Teacher spread0.186 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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