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Record W2791123257 · doi:10.1504/ijhvs.2018.10011869

Development of hybrid electric heavy-duty truck with self-propelled trailer

2018· article· en· W2791123257 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

VenueInternational Journal of Heavy Vehicle Systems · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTruckTrailerHeavy dutyEngineeringAutomotive engineeringAeronauticsAerospace engineeringMarine engineering

Abstract

fetched live from OpenAlex

A new architecture of hybrid drivetrain for tractors and semi-trailers is proposed. The goal is to utilise the maximum capability of the hybrid electric tractor and semi-trailer and to enhance the traction and fuel consumption efficiencies. This drivetrain architecture employs a self-propelled trailer so that the tractive effort is shared between the tractor and the semi-trailer. A comprehensive model of the vehicle, including the drivetrain model, is developed and a fuzzy logic controller (FLC) is used to design the power management system (PMS). Simulation runs of the computer model are performed on two standard driving cycles to evaluate both the traction efficiency and fuel consumption of the proposed hybrid drivetrain architecture. The comprehensive results obtained indicate that the proposed self-propelled trailer drivetrain presents a superior advantage over the non-hybrid drivetrains and also the typical hybrid drivetrain for heavy vehicles.

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 score0.399
Threshold uncertainty score0.604

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 science0.0010.000
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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