Yannick Rauch

Das Lehr-/Lernkonzept Learning by Brewing verknüpft die theoretische Ausbildung in der Automatisierungstechnik mit praxisnaher Anwendung an einer experimentellen Brauanlage. Durch eine enge Verzahnung von Vorlesung und Labor erwerben Studierende nicht nur technische Kompetenzen in Prozessleittechnik, Sensorik und Aktorik, sondern entwickeln auch ihre sozialen Kompetenzen durch Kleingruppenarbeit weiter. Ein zentrales Element ist die Entwicklung, Umsetzung und Validierung eines vollständigen Automatisierungssystems. Die optionale Veranstaltung BRAUtomation erlaubt zudem eine kreative, wettbewerbsbasierte Vertiefung. Das Konzept fördert interdisziplinäres Denken, motiviert durch hohe Praxisnähe und soll zur Steigerung der Attraktivität des Studienschwerpunkts Automatisierungstechnik beitragen.

The usage of batteries and supercapacitors in the field of electric vehicles is becoming increasingly prominent in both research and development. Due to the complementary advantages of the two systems, high energy density, and high power density, a combined battery/supercapacitor system offers potential. To effectively utilise the potential of such a hybrid energy storage system (HESS), one requires an intelligent energy management system (EMS). The EMS is responsible for controlling the electrical power between the battery and the supercapacitor in such a way that the required power can be optimally distributed at all times (currently and in the future). For this purpose, the energy management system utilises information on the driving route and, based on this information, shall calculate a global strategy for the continuous power distribution. The controlled power distribution should take place in real time and be robust against discrepancies so that unpredictable or unreliably predictable events do not significantly influence the functionality. For the implementation of the concept, a rule-based power distribution is implemented in combination with a predictive energy management. Here, the energy management is combined with a rule-based strategy calculation based on data on the route to be driven with a global optimization for the calculation of a route-specific strategy. Depending on the selected objective, the increase in energy efficiency, or lifetime, the operation of the power control is optimised. Due to the functional separation, the continuous power control can operate in real time, while more computational time can be spent on the calculations of the power management strategy, which accordingly does not need to be executed in real time. The results show that by using the presented EMS, especially in combination with a route-specific parameterisation, a significant effect on the energy efficiency and/or battery lifetime can be achieved. The average battery energy consumption can be reduced by up to 9.14% on urban routes. Regarding battery lifetime, the average battery usage can be reduced up to 13.35% and the battery energy losses even up to 62.72%.