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Record W2732097113 · doi:10.18260/1-2--13382

Development Of A Hydrogen Powered Hev As An Interdisciplinary Laboratory Project

2020· article· en· W2732097113 on OpenAlexaboutno aff
Tim Maxwell, Michael Parten

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsnot available
FundersFord Motor CompanyU.S. Department of Energy
KeywordsBattery electric vehicleAutomotive engineeringAutomotive industryBattery (electricity)Hybrid vehicleElectric motorElectric vehicleEngineeringElectrical engineeringPower (physics)Aerospace engineering

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2003 Development of a Hydrogen Powered HEV as an Interdisciplinary Laboratory Project Micheal Parten, Timothy Maxwell Electrical and Computer Engineering/Mechanical Engineering Texas Tech University I Introduction Over the past several years, Texas Tech University’s Advanced Vehicle Engineering Laboratory (AVEL) has converted six conventional vehicles to hybrid electric (HEVs) and alternative fueled vehicles for the various Vehicle Challenges sponsored by the U.S. Department of Energy (DOE), the three major U.S. automobile manufacturers, the Society of Automotive Engineers and Natural Resources Canada. Of particular interest today is the popularity of full sized sport utility vehicles (SUV). These vehicles are reversing the trends, over the last few years, of reduced emissions and improved fuel economy. In line with these problems, Texas Tech University is developing a hybrid Ford Explorer powered by 2.3 liter spark ignition engine, running on hydrogen, in parallel hybrid configuration with a 75 kilowatt induction motor. Two nickel metal hydride battery packs connected in parallel at 300 Volts DC nominal provide 13 Amp hours to drive the electric motor. The hybrid design maximizes efficiency with electric assistance adding to the vehicle’s performance during high engine loads and maintains a self sustaining charge through regeneration at times of low power train demands. A National Instruments' LabVIEW system is used to monitor and control the vehicle. The development of the vehicle is a multidisciplinary project with students from mechanical engineering, electrical engineering and computer science involved. The majority of the undergraduate team members are enrolled in a two-semester senior design sequence. However, graduate students and volunteers also participated in the program. Faculty advisors from both electrical and mechanical engineering provide guidance for the team. Large, interdisciplinary team projects like this can give students a more complete understanding of interfacing, decision making and cooperation. II. Hydrogen as a Fuel Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright  2004, American Society for Engineering Education

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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