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Record W7133371684

Energy management of a hybrid locomotive equipped with fuel cell, batteries, supercapacitors and intermittent access to electric network

2021· article· en· W7133371684 on OpenAlexfundno aff
Diana Sofía Mendoza Contreras

Bibliographic record

VenueUniversidad Industrial de Santander · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsnot available
FundersUniversidad Industrial de SantanderGovernment of Canada
KeywordsElectric energyEnergy management systemWork (physics)Energy management
DOInot available

Abstract

fetched live from OpenAlex

Este trabajo de investigación de maestría propone una estrategia de gestión de energía (EMS por sussiglas en inglés) para una locomotora híbrida de modo dual equipada con pila de combustible, supercondensadores ybaterías, y acceso intermitente a una catenaria aérea electrificada. La EMS modular define la distribución de energíaentre las fuentes de energía respetando las restricciones en las variables eléctricas del sistema. La estrategia estáinspirada en el diagrama de Ragone, el cual relaciona las características de potencia y energía de cada una de lasfuentes. La EMS no considera información o predicciones del consumo de carga futuro. La EMS en tiempo realpropuesta tiene como objetivo reducir una función de costo que considera el costo del hidrógeno, la electricidadconsumida de la red y la degradación de las fuentes de energía. Esta estrategia propuesta se centra en maximizar laenergía recuperada durante el frenado. El trabajo de investigación también introduce una metodología original paraajustar los valores del conjunto de parámetros de la EMS, la cual se basa en el método experimental. Se emplean doscasos de estudio para evaluar la EMS propuesta. Los resultados muestran que existe una oportunidad real de aumentar la energía recuperada durante el frenado si se realiza una EMS apropiada.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.784

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 head0.015
GPT teacher head0.201
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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueUniversidad Industrial de SantanderSame topicElectric and Hybrid Vehicle TechnologiesFrench-language works237,207