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Record W2779067928 · doi:10.3390/en11010034

Research on a Novel Hydraulic/Electric Synergy Bus

2017· article· en· W2779067928 on OpenAlexaff
Kegang Zhao, Zhihao Liang, Yanjun Huang, Hong Wang, Amir Khajepour, Yuke Zhen

Bibliographic record

VenueEnergies · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsPowertrainAutomotive engineeringFuel efficiencyMATLABHydraulic accumulatorDriving cycleElectric vehicleAccumulator (cryptography)Battery (electricity)Hybrid vehicleRegenerative brakeComputer scienceEngineeringTorquePower (physics)Mechanical engineering

Abstract

fetched live from OpenAlex

In recent years, increasing concerns regarding environmental pollution and requirements for lower fossil fuel consumption have increased interest in alternative hybrid powertrains. As a result, this paper presents a novel hydraulic/electric synergy powertrain with multiple working modes. The three energy sources (i.e., engine, battery, and hydraulic accumulator) in this configuration are regulated by a dual planetary gear set to achieve optimal performances. This paper selects the component sizes of a hybrid electric vehicle (HEV), a hydraulic hybrid vehicle (HHV), and a hydraulic/electric synergy vehicle (HESV), based on the dynamic performance of a target vehicle (TV). In addition, this paper develops the forward simulation models of the four aforementioned vehicles in the MATLAB/Simulink/Driveline platform, in which the fuel economy simulations are carried out in relation to the Chinese urban bus cycle. The simulation results show that the fuel consumption of the three hybrid vehicles is similar, but much better than, that of the TV. Finally, based on the operating cost calculations over a five-year working period, the lowest cost ranges of the three hybrid vehicles are determined, which provides a method for choosing the optimal hybrid scheme.

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 score0.679
Threshold uncertainty score0.536

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 head0.050
GPT teacher head0.308
Teacher spread0.258 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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