MétaCan
Menu
← Back to cohort
Record W2622739070 · doi:10.18260/1-2--10520

Instrumentation Of A Pem Fuel Cell Vehicle

2020· article· en· W2622739070 on OpenAlexaboutno aff
T. Maxwell, Michael Parten

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
FundersU.S. Department of Energy
KeywordsProton exchange membrane fuel cellBattery (electricity)Automotive engineeringStack (abstract data type)Electric vehicleAuxiliary power unitElectricityZero emissionElectrical engineeringRange (aeronautics)Hydrogen fuelDriving rangeElectric powerElectric-vehicle batteryDC motorPower (physics)EngineeringComputer scienceFuel cellsVoltageAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Main Menu Session XXXX Instrumentation of a PEM Fuel Cell Vehicle Bruce Sun, Wallace Turner, Micheal Parten Tim Maxwell Electrical and Computer Engineering Mechanical Engineering Texas Tech University I. Introduction Electric vehicles have long held the promise of zero emission vehicles. However, battery powered electric vehicles have not been accepted by the general public, in large part, because of their very limited range. A hydrogen-based, fuel cell could provide the power necessary to give an electric vehicle the same range as a modern gasoline powered vehicle. In this case, a fuel cell is a device that converts hydrogen into electricity by a simple oxidation reaction. The products of the electrochemical process are electricity, heat and water. In a fuel cell powered vehicle, an equivalent series hybrid power train provides the driving power to the wheels. Both the battery pack and the PEM fuel cell system supply power to the motor and motor controller. Since the output electrical power of the fuel cell stack is designed to exceed the average power demands of the vehicle, the batteries can be charged while driving. The range of the vehicle is then tied to the amount of hydrogen or fuel that is on board. A fuel cell powered vehicle consists of the integration of many complex nonlinear systems. The power train, generally, contains a proton exchange membrane (PEM) fuel cell stack with its accessories, a DC/DC converter, battery pack, motors and motor controllers. A PEM fuel cell stack is, itself, a complex electrochemical system.1- 4 Over the past several years, Texas Tech University’s Advanced Vehicle Engineering Laboratory (AVEL) has converted five conventional vehicles to 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.5-9 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, recent work at AVEL has included the conversion of a 2000 model General Motors Suburban to a fuel cell powered 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 team members are enrolled in a two-semester senior design sequence. However, some graduate students and volunteers also participated in the program. Faculty advisors from both electrical and mechanical engineering provide guidance for the team. The fuel cell’s performance is directly related to a large number of factors, which must be monitored and controlled. In this application, a modular LabVIEW Virtual Instrument is used to Main Menu

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)0.001
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0170.008

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 head0.008
GPT teacher head0.170
Teacher spread0.163 · 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

Explore more

Same topicFuel Cells and Related Materials→French-language works237,207→