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Record W2603745997 · doi:10.4122/dtu:206

Pneumatic Regenerative Braking System for Vehicle

2019· article· en· W2603745997 on OpenAlexaboutno aff
Kristian Uldall Kristiansen, Claus D. Jensen

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive engineeringCompressed airLimitingBrakeEngine brakingEngineeringFossil fuelWork (physics)Air compressorGas compressorEfficient energy useFuel efficiencyMechanical engineeringWaste managementElectrical engineering

Abstract

fetched live from OpenAlex

ABSTRACT\nThroughout the later years there has been an increasing focus on the development of\ntechnologies which can reduce the need for fossil fuel without limiting the yielding capacity.\nCars have a great deal of the focus about the use of oil. With 10% of the world's populations\nowning a car and expectation of the world car park tripled in 20501 it is crucial to limit the use\nof fuel for vehicles. The efficiency improvement of vehicles is how to limit the losing of\nenergy (e.g. braking, idle and external energy demanding utilisation) and especially the loss\nof energy while braking and idling is staked to 40% of the total energy use. Several of these\nhybrid techniques are continuously under development and they "competes" against each\nother with the purpose of limiting the need of fossil fuel and to run a vehicle with best overall\nefficiency. Equal for all hybrid technologies are that they avoid the loss of energy.\nPneumatic hybrid vehicle (PHV) is a technique that could work without adding advanced and\nexpensive materials or components – thereby it would be simpler and cheaper. The option\nto use air as thrust giving fuel has no pollution and letting a compressor inverting brake\nenergy into compressed air stored in air tank - it would be free to run. With automakers\ndownsizing their engines to reduce engine friction, the kinetic regenerated compressed air\nhas better circumstances as thrust giving fuel. To store compressed air and use it in e.g.\nfour-stroked ICE, it won't have any combustion gasses while running on compressed air.\nTherefore, to achieve higher efficiency the engine would have to run as two-stroke. To\ncontrol this, the valve control is a complex matter [4] duo to the shift from combustion and\npneumatic mode. Research by L. Guzella et al.2 has shown fuel improvements up to 35% on\ngasoline engine approximately the same efficiency as electric hybrid but by far less cost and\nless complex technique. By implementing the pneumatic technique in the ICE the interaction\nwould be smooth and less complex and costly as with flywheel, electric and fuel cell -\nthereby improves the driveability for the hybrid vehicle.\nDownsizing improves fuel consumption 12-17%3 and lowers emissions by basically fitting a\nsmaller engine with a turbocharger. A side effect from turbocharged downsized ICE's is\nturbo lag at lower revs. With storage of compressed air, turbo lag can be minimized thus,\ndownsizing and pneumatic hybrid shows promising.\nThe result of pneumatic hybrid is: No external implementation exept pressure tank, made by\neasy recyclable materials as: cast iron and aluminium, low costs.\n1 http://www.fiafoundation.org/50by50/documents/50BY50_report.pdf page 5.\n2 Amir Fazeli, Amir Khajepour, Cecile Devaud. Department of Mechanical and Mechatronic Engineering,\nUniversity of Waterloo, Waterloo, Canada. Applied Energy 88 (2011) 2955-2966\n3 Development of a 4-Cylinder Gasoline Engine with a Variable Flow Turbo-charger" SAE TECHNICAL\nPAPER SERIES 2007-01-0263\nNobuhiro Ito, Tohru Ohta, Ryuji Kono, Satoshi Arikawa and Takaki Matsumoto

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.205
Teacher spread0.187 · 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.

Study designNot applicable
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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicElectric and Hybrid Vehicle TechnologiesFrench-language works237,207