A Computer Tool Using OpenModelica for Modelling CO2 Emissions in Driving Tests

Abstract

The transport sector is one of the main barriers to achieving the European Union’s climate protection objectives. Therefore, more and more restrictive legal regulations are being introduced, setting out permissible limits for the emission of toxic substances emitted into the atmosphere, promoted biofuels and electromobility. The manuscript presents a computer tool to model the total energy consumption and carbon dioxide emissions of vehicles with an internal combustion engine of a 2018 Toyota Camry LE. The calculation tool is designed in the OpenModelica environment. Libraries were used for this purpose to build models of vehicles in motion: VehicleInterfaces, EMOTH (E-Mobility Library of OTH Regensburg). The tool developed on the basis of actual driving test data for the selected vehicle provides quantitative models for the instantaneous value of the fuel stream, the model of the instantaneous value of the carbon dioxide emission stream as a function of speed and the torque generated by the engine. In the manuscript, the tests were conducted for selected driving cycles tests: UDDS (EPA Urban Dynamometer Driving Schedule), HWFET (Highway Fuel Economy Driving Schedule), EPA US06 (Environmental Protection Agency; Supplemental Federal Test Procedure (SFTP)), LA-92 (Los Angeles 1992 driving schedule), NEDC (New European Driving Cycle), and WLTP (Worldwide Harmonized Light-Duty Vehicle Test Procedure). Using the developed computer tool, the impact on CO2 emissions was analyzed in the context of driving tests with four types of fuels: petrol 95, ethanol, methanol, DME (dimethyl ether), CNG (compressed natural gas), and LPG (liquefied petroleum gas).

Analysis of the Possibility of Using Neural Networks to Monitor the Technical Efficiency of Diesel Engines During Operation

“…The authors of the manuscript have been using neural networks and genetic algorithms in their research for many years. They used the support of artificial intelligence methods, for example, to model numerical flows in turbine stages [51,52], to model vehicle driving tests [53,54]. The authors of the manuscript in published scientific papers have analysed a wide range of different approval procedures carried out on vehicles with compression ignition and spark ignition engines, taking into account different fuel mixtures and equipment with assistance systems, e.g.…”

Car Engines Comparative Analysis: Sustainable Approach

“…Therefore, the term "green transport" has become very popular because of its association with the reduction in the negative impact of this economic sector on the environment, through the use of the latest technologies [13]. The transition from emission-based to zero-emission, electric-propulsion-type vehicles, is not only a challenge for car manufacturers [14] but also an opportunity for a step towards SD for whole countries [2,15]. It is also a search for new business models and development strategies for organizations [16].…”

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

“…The electromobility is developed through the Industry 4.0 features [14,15], which can be measured by the growing number of electric cars or electric vehicles (EVs) [16,17] and charging stations [18,19]. The charging stations are gathered in off-grid or on-grid networks [20,21] that require continuous data analysis [22,23] in electricity distribution or sale processes [24,25]. It should also be considered that electric vehicles vary in terms of software [26,27], technical specifications [28,29], performance [30,31], and even charging methods [16,32].…”