Modeling the Impact of Alternative Fuel Properties on Light Vehicle Engine Performance and Greenhouse Gases Emissions

Abstract

The present-day transport sector needs sustainable energy solutions. Substitution of fossil-fuels with fuels produced from biomass is one of the most relevant solutions for the sector. Nevertheless, bringing biofuels into the market is associated with many challenges that policymakers, feedstock suppliers, fuel producers, and engine manufacturers need to overcome. The main objective of this research is an investigation of the impact of alternative fuel properties on light vehicle engine performance and greenhouse gases (GHG). The purpose of the present study is to provide decision-makers with tools that will accelerate the implementation of biofuels into the market. As a result, two models were developed, that represent the impact of fuel properties on engine performance in a uniform and reliable way but also with very high accuracy (coefficients of determination over 0.95) and from the end-user point of view. The inputs of the model are represented by fuel properties, whereas output by fuel consumption (FC). The parameters are represented as percentage changes relative to standard fossil fuel, which is gasoline for spark ignition (SI) engines and diesel for compression ignition (CI) engines. The methodology is based on data-driven black-box modeling (input-output relation). The multilinear regression was performed using the data from driving cycles such as the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and New European Driving Conditions (NEDC). The FC of SI engines proved to be dependent on mass-based Net Calorific Value (NCV), Research Octane Number (RON), oxygen content and density. However, CI engines performance is affected by NCV, density and Cetane Number (CN). The models were additionally subject to quantitative analysis, where input parameters in both models turned out to be statistically significant (p-value below 5%). Additionally, the validation stage consisted of residual analysis confirmed the accuracy of both models. The GHG part estimates the change of carbon dioxide emissions based on fuel consumption, which represents the tailpipe emissions.