We present an optimization model for the passenger car vehicle fleet transition-the time-dependent fleet composition-in Germany until 2050. The goal was to minimize the cumulative greenhouse gas (GHG) emissions of the vehicle fleet taking into account life-cycle assessment (LCA) data. LCAs provide information on the global warming potential (GWP) of different powertrain concepts. Meta-analyses of batteries, of different fuel types, and of the German energy sector are conducted to support the model. Furthermore, a sensitivity-analysis is performed on four key influence parameters: the battery production emissions trend, the German energy sector trend, the hydrogen production path trend, and the mobility sector trend. Overall, we draw the conclusion that-in any scenario-future vehicles should have a plug-in option, allowing their usage as fully or partly electrical vehicles. For short distance trips, battery electric vehicles (BEVs) with a small battery size are the most reasonable choice throughout the transition. Plug-in hybrid electric vehicles (PHEVs) powered by compressed natural gas (CNG) emerge as promising long-range capable solution. Starting in 2040, long-range capable BEVs and fuel cell plug-in hybrid electric vehicles (FCPHEVs) have similar life-cycle emissions as PHEV-CNG.

As the electrification of the transportation sector advances, fleet operators have to rethink their approach regarding fleet management against the background of limiting factors, such as a reduced range or extended recharging times. Charging infrastructure plays a critical role, and it is worthwhile to consider its planning as an integral part for the long-term operation of an electric vehicle fleet. In the category of fixed route transportation systems, the predictable character of the routes can be exploited when planning charging infrastructure. After a prior categorization of stakeholders and their respective optimization objectives in the sector coupling domain, a cost optimization framework for fixed route transportation systems is presented as the main contribution of this work. We confirm previous literature in that there is no one-fits-all optimization method for this kind of problem. The method is tested on seven scenarios for the public transport operator of Darmstadt, Germany. The core optimization is formulated as a mixed integer linear programming (MILP) problem. All scenarios are terminated by the criterion of a maximum solving time of 48 h and provide feasible solutions with a relative MIP-gap between 7 and 24%.

As digitization and Industry 4.0 progress, the need for smart products and innovative business models increases. This contribution presents the underlying novel concept of the collaborative course “Smart Products, Engineering and Services”. The objective is to enable students of mechanical engineering to develop and work with smart products, and to derive possible business models for their use. A combination of traditional lectures, flipped classroom exercises and a development project characterizes the unique character of the course. The presented topics range from the basics of sensing machine elements, intelligent mechatronic systems and the use of artificial intelligence in the latter. Further, it comprises product development methods such as agile development, V&V methods and the usage of rapid manufacturing technologies. The flipped classroom exercises serve as preparation for the project work and allow students to gain practical experience with additive manufacturing processes as well as cyber-physical systems. As part of the project work, students develop a smart product which must complete the control task of balancing a body on vertically excited surface by minimizing the bodies movement. For this purpose, the kinematics, the controller, and the usage of a force-measuring ball bearing as a sensing machine element are predetermined. Missing components must be designed and manufactured in a Makerspace using 3D printing or laser cutting and the developed controller must be implemented. The smart product is finally tested on a specially developed test rig.