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 CO2 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 CO2 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 CO2 emissions were analyzed in driving tests for the given analyzed vehicle.