Synthesis of representative driving cycles based on large amounts of measurement data

Synthetic driving cycles have been used in the testing and development of vehicle powertrains for several decades. These synthetic driving cycles are used to represent larger quantities of measured operating data in compressed form and to describe a relevant driving profile. Synthetic driving cycles are indispensable for testing vehicles or individual drive components on a test rig, as it would be very time-consuming and costly, or even impossible, to carry out a test using the entirety of the originally available operating data. But synthetic driving cycles are also important for simulative computer-aided powertrain development in order to reduce the computational effort involved in evaluating many different parameterizations of a powertrain.

The stochastic synthesis of driving cycles from fleet data that are as short and representative as possible is a key area of research at the IMS, and an extensive toolbox has been built. The methods work multidimensionally and can take into account the interdependence of multiple signals in realistic test cycles.

Our offer:

  • Synthesis of representative driving cycles for test bench trials or simulative evaluations from large amounts of driving data.
  • Classification and systematic comparison of driving profiles and special customer driving cycles to reference profiles (e.g. WLTC).


Eßer, Arved ; Zeller, M. ; Foulard, Stéphane ; Rinderknecht, Stephan (2018): Stochastic Synthesis of Representative and Multidimensional Driving Cycles. In: SAE International Journal of Alternative Powertrains, 7 (3), S. 263-272. ISSN 2167-4205

Eßer, Arved ; Rinderknecht, Stephan (2020): Process for the Validation of Using Synthetic Driving Cycles Based on Naturalistic Driving Data Sets.. Rhodes, Greece, IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 20-23 Sept. 2020, ISBN 978-1-7281-4149-7, DOI: 10.1109/ITSC45102.2020.9294369