Prediction of heavy-duty engine performance for renewable fuels based on fuel property characteristics

Abstract

Renewable drop-in fuels can bring timely and efficient defossilization of the current fleet of heavy-duty vehicles. In the present study, different blends of renewable components with standard diesel were analyzed in the context of end-use performance. Based on experimental data from driving cycles, a novel modeling approach was applied to develop a state-of-the-art mathematical model that enables an accurate estimation of fuel consumption and tailpipe CO2 emissions from heavy-duty vehicles relying solely on fuel properties. The predictions revealed strong agreements with experimental data confirmed by the high coefficient of determination (0.975). The final model represents fuel properties’ collective impact on heavy-duty vehicle’s fuel economy over the Braunschweig cycle where heating value, density, and cetane number showed the strongest impact (p-values <0.01). The developed model was applied to simulate the effect of alternative diesel fuels on end-use performance. The increase in mass-based fuel consumption was observed for FAME (14%), oxymethylene ether blends (up to 65%), moderate contents of butanol and pentanol blends (up to 11%), while neat HVO improved fuel economy (6%). The introduced model can be applied to the assessment of renewable liquid fuel blends in heavy-duty transport and serves as a support for industry and decision-makers.