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Record W4412536680 · doi:10.1109/ojia.2025.3591144

Hamiltonian-Based Approach to Enhance the Stability of Hybrid Fuel Cell and Supercapacitor Sources

2025· article· en· W4412536680 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Open Journal of Industry Applications · 2025
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSupercapacitorHamiltonian (control theory)Fuel cellsStability (learning theory)Materials scienceComputer scienceChemical engineeringChemistryEngineeringMathematicsCapacitanceMathematical optimizationElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

This paper aims to study an improved large-signal stability for fuel cell (FC) and supercapacitor (SC) hybrid sources, employing the enhanced Hamiltonian control law. This novel approach addresses the inherent challenges in the dynamic operation of such hybrid systems, characterized by rapid load changes [i.e., constant power load (CPL)] and energy fluctuations. Grounded in energy-based control theory, the Hamiltonian control law accurately manages the energy exchange between the FC, SC, and external load aiming to improve system stability and response efficiency. A comprehensive test bench setup, including a real FC, an SC bank, and programmable loads to simulate the electrical load [i.e., CPL, constant resistive load (CRL), and constant current load (CCL)], was developed to evaluate performance under various operational conditions. The results demonstrate that Hamiltonian-based control significantly enhances the system's damping properties, ensuring a smoother response to load variations and enhanced stability across different scenarios.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.285

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 head0.014
GPT teacher head0.252
Teacher spread0.239 · 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