Thomas Rudolf studied electrical engineering and information technology at the Karlsruhe Institut of Technology (KIT) and Chalmers University of Technology in Gothenburg, Sweden. During his studies in the specialized fields of information and automation he focused on control engineering, signal processing and system optimization. He concluded his studies with his master thesis about the design of a robust position control and a differential flatness-based velocity control for a four-wheel drive, four-wheel steering electrical vehicle („Entwurf einer robusten Positionsregelung mit kaskadierter flachheitsbasierter Geschwindigkeitsregelung eines radselektiv angetriebenen Elektrofahrzeugs“) at the Institute for Control Systems (IRS).
Since January 2018, he has been employed as a research scientist at the FZI Research Center for Information Technology in the division of Embedded Systems and Sensors Engineering (ESS).
Predictive Operating Strategies for Alternative Powertrains
In the context of electromobility, various alternative drive trains are being discussed in research as well as for industrial applications. For example, battery electric vehicles demonstrate advantages in urban applications whereas a hydrogen hybrid powertrain shows its potential in long range applications. Hence, different requirements are posed regard performance, range, energy sources and buffer storages.
For sustained operations, a high energy efficiency must be guaranteed and the lifespan of the powertrain must be prolonged. Both could be optimized by predictive operating strategies including route data and other information. An additional increase in efficiency is expected by the inclusion of auxiliary units within an overall system optimization problem.
Model-based methods for intelligent power distribution as well as optimization criteria for energy efficiency and thermal load could lead to success. In recent years, methods for increased energy efficiency of hybrid vehicles with combustions engines and battery electric vehicles have been researched. However, research on life cycle increasing strategies through inclusion of auxiliaries into the overall optimization problem is lacking.
The objectives of this work are dedicated optimization criteria as well as methods and algorithms for dealing with such a nonlinear mixed integer optimization problem due to discrete control variables in these systems.
Rudolf, T.; Bischof, F.; Schwab, S.; Hohmann, S.
2019. 6th SINO-EU Doctoral School for Sustainability Engineering (SESE 2019), Lisbon, Portugal, July 22–24, 2019