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Record W2014935698 · doi:10.1109/vppc.2012.6422779

Fault-operation modes of a highly redundant military HEV

2012· article· en· W2014935698 on OpenAlexafffund
Loïc Boulon, Alain Bouscayrol, Daniel Hissel, Olivier Pape, Marie‐Cécile Péra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersTehran University of Medical Sciences and Health ServicesFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsControl reconfigurationRedundancy (engineering)Electric vehicleComputer scienceArchitectureReliability engineeringReliability (semiconductor)AxleFault (geology)Energy managementEngineeringEnergy (signal processing)Power (physics)Embedded system

Abstract

fetched live from OpenAlex

This paper presents the fault-operation modes of a military hybrid electric vehicle so-called DPE 6x6. This vehicle has a very specific architecture (high redundancy) to ensure a high reliability of the system. This work is based on the Energetic Macroscopic Representation methodology. In previous publications, the system modeling and control have been presented and now, this paper shows the relevance of the architecture and of the control structure in case of failures. The failure of a powered axle is studied. The simulations show the impact of the failures on the vehicle with and without energy management reconfiguration.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.201
Teacher spread0.191 · 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 designSimulation or modeling
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
Published2012
Admission routes2
Has abstractyes

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