Browse Publications Technical Papers 2019-26-0346

Phenomenological Traffic Simulation as a Basis for an RDE Development Methodology 2019-26-0346

Pollutant emissions and the fuel/energy consumption of vehicles in real driving conditions are increasingly becoming the focus of public and legislative attention. According to the Euro 6d standard, vehicles must comply with emission limits on the test bed and on the road (Real Driving Emissions, RDE). This paper discusses a methodology that enables RDE compliance and robustness testing of engines and propulsion systems using a new phenomenological traffic simulation approach.
The approach is based on virtual test driving and can be used in pure simulation as well as for testing at test beds based on the road-to-rig concept. A real route is digitized and a vehicle model (digital twin) of the target vehicle is built that models the driving resistances and vehicle dynamics properties of the real car. Finally, a virtual driver (driver model) drives the vehicle model on the digitized route considering also traffic objects, road signs and traffic lights. In order to take a diverse range of traffic situations into account, a phenomenological traffic simulation approach was developed that does not require a definition of traffic objects.
The traffic situations are modeled using an indirect target speed input for the driver model by means of additional road signs on the route. Within these boundary conditions, the driver model decides on the dynamics with which the virtual vehicle is driven through traffic. The traffic situations are modeled using route sections that originate from a real driving database. These sections are classified, weighted in terms of traffic density and route type, and distributed along the digitized route.
In contrast to previous approaches, this method allows for a flexible and realistic generation of real driving scenarios taking into account the physics of the vehicle and the 3-D road characteristics as well as the driver behavior. The results obtained with this approach show a very good correlation with real driving measurement data.


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