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Record W2102236750 · doi:10.1109/isie.2008.4677252

An improved powertrain topology for fuel cell-battery-ultracapacitor vehicles

2008· article· en· W2102236750 on OpenAlexaff
Jennifer Bauman, Mehrdad Kazerani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPowertrainBattery (electricity)Network topologySupercapacitorTopology (electrical circuits)Automotive engineeringFuel cellsEnergy storageComputer scienceElectric vehicleEngineeringElectrical engineeringPower (physics)CapacitanceTorqueComputer network

Abstract

fetched live from OpenAlex

A combined battery-ultracapacitor energy storage system is a promising possibility for use with fuel cell vehicles because it combines the advantages of batteries and ultracapacitors. This paper reviews some popular topologies for fuel cell-battery-ultracapacitor powertrains, and presents a new topology which minimizes many of the disadvantages of the other topologies. Simulation results for the new topology are presented and are compared to results for a common fuel cell-battery-ultracapacitor topology. The new topology is shown to improve the fuel economy and reduce the cost of the vehicle.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.611

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 science0.0000.000
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 head0.011
GPT teacher head0.212
Teacher spread0.202 · 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

Citations12
Published2008
Admission routes1
Has abstractyes

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