Search Results

Advanced driver assistance systems like cooperative adaptive cruise control (CACC) are designed to exploit information provided by vehicle-to-vehicle (V2V) and/or infrastructure-to-vehicle (I2V) communication systems to achieve desired objectives such as safety, traffic fluidity or fuel economy. In a day to day traffic scenario, the presence of unknown disturbances complicates achieving these objectives. In particular, CACC benefits in terms of fuel economy require the prediction of the behavior of a preceding vehicle during a finite time horizon. This paper suggests an estimation method based on actual and past inter-vehicle distance data as well as on traffic and upcoming traffic lights. This information is used to train a set of nonlinear, autoregressive (NARX) models. Two scenarios are investigated, one of them assumes a V2V communication with the predecessor, the other uses only data acquired by on-board vehicle sensors.

Especially in view of more and more stringent emission legislation in passenger cars it is required to reduce the amount of pollutants. In the case of Diesel engines mainly NOx and PM are emitted during engine operation. The main influence factors for these pollutants are the in-cylinder oxygen concentration and the injected fuel amount. Typically the engine control task can be divided into two separate main parts, the fuel and the air system. Commonly air system control, consisting of a turbocharger and exhaust gas recirculation control, is used to provide the required amount of oxygen and address the emission targets, whereas the fuel is used to provide the desired torque. Especially in transient maneuvers the different time scales of both systems can lead to emission peaks which are not desired. Against this background in this work instead of the common way to address the air system, the fuel system is considered to reduce emission peaks during transients.