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Analysis and Design of an Isolated Converter with Embedded EIS Function for Fuel Cell Stack Considering Low-Frequency Oscillations

2020· article· en· W3101422000 on OpenAlexaff
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
KeywordsStack (abstract data type)Electrical impedancePowertrainComputer scienceAutomatic frequency controlEnergy storageFuel cellsElectronic engineeringPower (physics)Control theory (sociology)EngineeringElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

This paper presents the analysis and design of an isolated dc-dc power conditioning converter (PCC) for use with a fuel cell stack (FCS) incorporating the function of online electrochemical impedance spectroscopy (EIS). The converter is designed considering the output characteristics of the FCS. The challenge of producing full-frequency-range perturbations with PCC based EIS is highlighted, where a focus of this work is related to the low-frequency perturbation generation and resolving the oscillations introduced by it. The oscillations propagated to the dc bus and load can cause severe disturbances in a vehicle powertrain. The problem is effectively tackled by guiding the oscillations from the load side to the primary side energy storage (PSES) with the designed PCC and its control. A detailed design case and its simulation results are presented to demonstrate the validity of the proposed approach.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.205
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2020
Admission routes1
Has abstractyes

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