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.