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An Auxiliary Converter for Fuel Cell Stack EIS using a Switched-Capacitor Converter Interfaced Supercapacitor as a Bidirectional Energy Buffer

2020· article· en· W3103565620 on OpenAlexaff
B Shadmand Mohammad, Jiabin Shen, Jiacheng Wang

Bibliographic record

VenueIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCapacitorSupercapacitorStack (abstract data type)MiniaturizationSwitched capacitorAuxiliary power unitBoost converterElectronic engineeringElectrical engineeringComputer scienceMaterials scienceEngineeringCapacitanceVoltageElectrodeChemistry

Abstract

fetched live from OpenAlex

Electrochemical impedance spectroscopy (EIS) is a promising tool for characterizing fuel cells. It was traditionally only applied to single cell or short stacks at low-power levels but was recently brought to high-power stacks due to a growing interest in performing in situ EIS without dismantling the stack. Converter-based EIS provides attractive solutions for this purpose. In this paper, an auxiliary EIS converter solution using a bidirectional energy buffer module composed of a switched-capacitor converter (SCC) and a supercapacitor is proposed. The module is designed towards having a more compact auxiliary converter unit. The design of the proposed module is investigated in detail and a comprehensive guideline is provided considering the probable limitations imposed by high EIS frequencies on this solution. The proposed system helps with the compactness and miniaturization of the entire auxiliary EIS converter and eliminating the potential problems of electrolytic capacitors such as bulkiness and limited lifetime due to impact of ripples. The effectiveness of the developed system is verified by simulation results.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.034
GPT teacher head0.227
Teacher spread0.193 · 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.

Study designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics SocietySame topicFuel Cells and Related MaterialsFrench-language works237,207