LAPSE:2023.30063
Published Article
LAPSE:2023.30063
A Computer Tool for Modelling CO2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels
April 14, 2023
Abstract
A driving cycle is a record intended to reflect the regular use of a given type of vehicle, presented as a speed profile recorded over a certain period of time. It is used for the assessment of engine pollutant emissions, fuel consumption analysis and environmental certification procedures. Different driving cycles are used, depending on the region of the world. In addition, drive cycles are used by car manufacturers to optimize vehicle drivelines. The basis of the work presented in the manuscript was a developed computer tool using tests on the Toyota Camry LE 2018 chassis dynamometer, the results of the optimization process of neural network structures and the properties of fuels and biofuels. As a result of the work of the computer tool, the consumption of petrol 95, ethanol, methanol, DME, CNG, LPG and CO2 emissions for the vehicle in question were analyzed in the following driving tests: Environmental Protection Agency (EPA US06 and EPA USSC03); Supplemental Federal Test Procedure (SFTP); Highway Fuel Economy Driving Schedule (HWFET); Federal Test Procedure (FTP-75−EPA); New European Driving Cycle (NEDC); Random Cycle Low (×05); Random Cycle High (×95); Mobile Air Conditioning Test Procedure (MAC TP); Common Artemis Driving Cycles (CADC−Artemis); Worldwide Harmonized Light-Duty Vehicle Test Procedure (WLTP).
Keywords
biofuel, car, fuel, neural model
Suggested Citation
Tucki K. A Computer Tool for Modelling CO2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels. (2023). LAPSE:2023.30063
Author Affiliations
Tucki K: Department of Production Engineering, Institute of Mechanical Engineering, Warsaw University of Life Sciences, Nowoursynowska Street 164, 02-787 Warsaw, Poland [ORCID]
Journal Name
Energies
Volume
14
Issue
5
First Page
1400
Year
2021
Publication Date
2021-03-04
ISSN
1996-1073
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Original Submission
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PII: en14051400, Publication Type: Journal Article
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LAPSE:2023.30063
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https://doi.org/10.3390/en14051400
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