Samenvatting

Abstract
Digital twinning of logistics vehicles can enhance decision making, to determine the right vehicle to use for the right duty (e.g. an EMS vehicle’s ability to negotiate the roundabouts in a route) and predict future tyre wear using vehicle telemetry (i.e. how much rubber is worn out and how will it wear based on current usage pattern). Multibody models of heavy-duty vehicles are suitable for such digital twinning of vehicle performance and operation. This paper presents an automated workflow to simulate heavy-duty vehicle performance by modelling real infrastructure and using realistic simulation inputs. Roundabouts in the planned missions are identified using map data features and their road width is extracted from satellite images using computer vision techniques. FMS data does not contain tyre forces to predict tyre wear, these are calculated by the digital twin models using MF Tyre models for individual tyres and FMS driver inputs like steer and throttle. The simulators used for this purpose are packaged and deployed into FMU models and Docker containers.