Compressed Driving Cycles Using Markov Chains for Vehicle Powertrain Design

In recent years, topics of digitalisation, urbanisation and sustainability are shaping future developments in the automotive industry and pose new challenges to the individual areas of vehicle development. In the design and simulation of multiple components in the vehicle, especially in powertrain, generically generated driving cycles play an important role since they reflect representative user behavior. Nowadays, driving cycles are mainly associated with the worldwide fuel consumption and emission tests, but they are nevertheless used in many fields of vehicle development. A design based on the currently used consumption and emission cycles proves to be unsuitable, especially for electric vehicles. This is reflected in the current discussion about the large discrepancy between the driving ranges achieved when using emission cycles and those under real driving conditions. In order to minimize the energy consumption of a vehicle, the main requirement is a good efficiency of the electric machine. Here, not the maximum efficiency of the machine is decisive, but the averaged overall system efficiency during real driving behavior. The electric machine must therefore be designed for the driving cycle. To optimize the use of electric machines in the future, the actual power requirements of future vehicle models must first be determined. In the course of this paper, a compressed vehicle class specific driving cycle will be created based on real driving data using Markov chains, which can be used for powertrain dimensioning.