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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.

The reduction of environmentally harmful gases and the ambitions to reduce the exploitation of fossil resources lead to stricter legislation for all mobile sources. Legislative development significantly affected improvements in emissions and fuel consumptions over the last years, mainly measured under laboratory conditions. But real world operating scenarios have a major influence on emissions and it is already well known that these values considerably differ from officially published figures [1]. There are regulated emissions by the European Commission by means of real driving scenarios for passenger cars. A methodology to measure real drive emissions RDE is therefore well approved for automotive applications but was not adapted for two-wheeler-applications yet [2]. Hence measurements have been performed on-road and on chassis dynamometer for motorcycles with the state of the art RDE measurement equipment to be prepared for possible future legislation.

In the course of the last few years a continuous increase of the injection pressure level of gasoline direct injection systems appeared. Today's systems use an injection pressure up to 200bar and the trend shows a further increase for the future. Although several benefits go along with the increased injection pressure, the disadvantages such as higher system costs and higher energy demand lead to the question of the lowest acceptable injection pressure level for low cost GDI combustion systems. Lowering injection pressure and costs could enable the technological upgrading from MPFI to GDI in smaller engine segments, which would lead to a reduction of CO2 emission. This publication covers the investigation of a low pressure GDI system (LPDI) with focus on small and low cost GDI engines. The influence of the injection pressure on the fuel consumption and emission behavior was investigated using a 1.4l series production engine.

The advantages of 2-stroke engines, high power and low weight, are in conflict with their disadvantages, high emissions and bad fuel economy. As these disadvantages are caused by the scavenging process, a reason for the problem can be analyzed by using three dimensional computational fluid dynamics simulation (3D CFD simulation). The scavenging losses can be dramatically reduced with a high pressure fuel injection strategy. The purpose of this strategy is to prevent a fuel concentration in the incoming charge and to reduce the fuel concentration inside the exhaust system. These advantages can only be successfully exploited with the application of an optimal injection strategy. This paper covers a spray study for a gasoline direct injection (GDI) high performance 2-stroke engine using the commercial CFD Code Fluent.