A Computer Tool for Modelling CO 2 Emissions in Driving Tests for Vehicles with Diesel Engines

Karol Tucki
Additional contact information
Karol Tucki: Department of Production Engineering, Institute of Mechanical Engineering, Warsaw University of Life Sciences, Nowoursynowska Street 164, 02-787 Warsaw, Poland

Energies, 2021, vol. 14, issue 2, 1-30

Abstract: The dynamic development of transport in recent decades reflects the level of economic development in the world. The transport sector today is one of the main barriers to the achievement of the European Union’s climate protection objectives. More and more restrictive legal regulations define permissible emission limits for the amounts of toxic substances emitted into the atmosphere. Numerical CO 2 modeling tools are one way to replace costly on-road testing. Driving cycles, which are an approximation of the vehicle’s on-road operating conditions, are the basis of any vehicle approval procedure. The paper presents a computer tool that uses neural networks to simulate driving tests. Data obtained from tests on the Mercedes E350 chassis dynamometer were used for the construction of the neural model. All the collected operational parameters of the vehicle, which are the input data for the built model, were used to create simulation control runs for driving tests: Environmental Protection Agency, Supplemental Federal Test Procedure, Highway Fuel Economy Driving Schedule, Federal Test Procedure, New European Driving Cycle, Random Cycle Low, Random Cycle High, Mobile Air Conditioning Test Procedure, Common Artemis Driving Cycles, Worldwide Harmonized Light-Duty Vehicle Test Procedure. Using the developed computer simulation tool, the impact on CO 2 emissions was analyzed in the context of driving tests of four types of fuels: Diesel, Fatty Acid Methyl Esters, rapeseed oil, butanol (butyl alcohol). As a result of the processing of this same computer tool, mass consumption of fuels and CO 2 emissions were analyzed in driving tests for the given analyzed vehicle.

Keywords: computer simulation; vehicle; engine; biofuel; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/2/266/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/2/266/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:2:p:266-:d:475522

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().