Relevance of Multidimensional Real-Driving Optimization for the Development of a Battery-Electric Vehicle 5.0: Multidimensional driving cycles based on fleet traffic data for the estimation of the energy demand of battery electric and conventional vehicles

摘要

Internal combustion engine (ICE) drivetrains are optimized with a focus on fuel consumption in standardized cycles to meet legal and customer requirements. Electric powertrains, however, are developed with other goals in mind. In this case, the electric range of the vehicle represents its main selling point. The electric range, however, is largely dependent on the overall electrical efficiency of the vehicle. Therefore, the efficiency also has to be considered when analyzing battery electric vehicles (BEV). In particular, the individual and real-driving profiles as well as external influences such as weather conditions, infrastructure, and road slope must be taken into account. In this work, multidimensional and representative driving cycles based on fleet driving data from Darmstadt are used to analyze the relevance of these considerations. The results confirm that the energy demand of battery electric vehicles in comparison to conventional vehicles is especially sensitive to external influences such as ambient temperature.