Electrified Drives for Automated Vehicles 2023-01-1679

This paper deals with the relationship between powertrain design and the requirements resulting from connected and automated driving. The questions addressed are how much powertrain design will change in regard to automated and connected driving and which powertrain in an automated vehicle will prove to be the optimum solution. To this end, a concept study is being conducted for a D-segment vehicle and multiple powertrain topologies ranging from non-electrified, mild-hybrids to plug-in hybrids and battery electric vehicles. The development processes required to address this issue is presented accordingly, as well as the necessary methods for systemic drive optimization, taking into account all requirements of the vehicle, the drive system and the components and their interactions with each other. The requirements resulting from connected and automated driving as well as their influences on vehicle and drive concepts are elaborated. The work focuses on automated driving functions developed at the Institute of Automotive Engineering of the Technische Universität Braunschweig, primarily on a “highway pilot”. The hardware architecture required to implement the automated driving functions leads to an increase in vehicle mass as well as additional power requirements. Automated driving also leads to changes in driving profiles during operation. On the basis of measurement data from the "highway pilot", representative driving cycles in terms of customer and function operation are created and used for drive optimization. The drives optimized for a vehicle with highway pilot lead to a significant reduction in fuel and energy consumption in customer operation. Electric vehicles are showing the highest savings potential (11% to 14%) followed by plug-in hybrids (5% to 14%). The consumption reduction potential also shows a strong dependence on the powertrain design and the powertrain topology. The combustion engine has a relatively large range with very high efficiencies, which is why the operating point distribution change due to automated driving only slightly influences the efficiency of the ICE, 34% without automated driving to 35% with automated driving. The results of the drive optimization are discussed in detail in the paper and the implications for future drive development are highlighted.