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Real time Performance Estimation for Energy Management of a Fuel Cell Hybrid Electric Vehicle

2021· article· en· W4211106985 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue2021 IEEE Vehicle Power and Propulsion Conference (VPPC) · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsEnergy managementAutomotive engineeringBattery (electricity)Electric vehicleReliability (semiconductor)Computer scienceEnergy consumptionFuel cellsEnergy (signal processing)Energy management systemFuel efficiencyIdentification (biology)Reliability engineeringEngineeringElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

Energy sources see their performances heavily impacted by operating conditions and aging, which makes their management more delicate. In this paper, an accurate Energy Management Strategy (EMS) for a Fuel Cell Hybrid Electric Vehicle (FC-HEV) is presented. The reliability of this energy management strategy is maintained using online parameter identification for battery and fuel cell models. The performances of the FC-HEV are estimated in real time using Recursive Least Square based filtering. These performances are then implemented in a real time energy management strategy to improve the vehicle consumption and lifetime.

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 score0.449
Threshold uncertainty score0.746

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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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