Optimal and Prototype Dimensioning of Electrified Drives for Automated Driving

Abstract

Electrified drives will change significantly in the wake of the further introduction of automated driving functions. Precise drive dimensioning, taking automated driving into account, opens up further potential in terms of drive operation and efficiency as well as optimal component design. Central element for unlocking the dimensioning potentials is the knowledge about the driving functions and their application. In this paper the implications of automated driving on the drive and component design are discussed. A process and a virtual toolchain for electric drive development from concept optimization to detailed dimensioning validation is presented. The process is subdivided into a concept optimization part for finding the optimal drive topology and layout and a detailed prototype environment, where more detailed component models can be assessed in customer operation to enable representative component dimensioning. Furthermore, the detailed simulation allows the drive investigation in representative customer operation as well as automated driving functions in terms of a software in the loop simulation. The process is used for the optimal dimensioning of a battery electric vehicle of the D-segment. The work focusses on a highway pilot function, developed at the Institute of Automotive Engineering of the Technische Universität Braunschweig. The optimal drive configuration can later be transferred to the prototype dimensioning. The simulation of automated driving function operation is based on a vehicle following scenario which employs statistical human behavior in the target vehicle and a sliding mode ACC in the ego vehicle. This methodology is particularly suitable for determining load spectra, which in turn can be used as test specifications for the strength simulation or endurance testing of the electric drive. Furthermore, simulation results can be used for the definition of representative cycles applicable for the concept optimization. The results of both processes will be compared and discussed in detail with an emphasis on efficiency, performance and load spectra.