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The hybrid-electric drivetrain permits a multitude of new control strategies like brake energy recuperation, engine start-stop operation, shifting of engine working point, as well as in some situations pure electric driving. Overall this typically allows a reduction of fuel consumption and therefore of carbon dioxide emissions. During the development process of the vehicle various drivetrain configurations have to be considered and compared. This includes decisions regarding the topology - like the position of the electrical machine in the drivetrain (e.g. at the gearbox input or output shaft), as well as the selection of the needed components based on their parameters (nominal power, energy content of the battery, efficiency etc.). To compare the chosen variants, typically the calculated fuel consumption for a given driving cycle is used.

As the number of different engine and vehicle concepts for powered-two wheelers is very high and will even rise with hybridization, the simulation of emissions and fuel consumption is indispensable for further development towards more environmentally friendly mobility. In this work, an adaptive artificial neural network based predictive model for emission and fuel consumption simulation of motorcycles operated in real world conditions is presented. The model is developed in Matlab and Simulink and is integrated into a longitudinal vehicle dynamic simulation whereby it is possible to simulate various and not yet measured test cycles. Subsequently, it is possible to predict real drive emissions RDE and on-road fuel consumption by a minimum of previous measurement effort.