@article{DachwaldSchmidtSeboldtetal.2003,
  author    = {Dachwald, Bernd and Schmidt, Tanja D. and Seboldt, Wolfgang and Auweter-Kurtz,},
  title     = {Flight Opportunities from Mars to Earth for Piloted Missions Using Continuous Thrust Propulsion / Schmidt, Tanja D. ; Dachwald, Bernd ; Seboldt, Wolfgang ; Auweter-Kurtz, Monika},
  publisher = {-},
  pages     = {1 -- 9},
  year      = {2003},
  language  = {en}
}
@inproceedings{BlankeSchmidtGoettscheetal.2022,
  author    = {Blanke, Tobias and Schmidt, Katharina S. and G{\"o}ttsche, Joachim and D{\"o}ring, Bernd and Frisch, J{\´e}r{\^o}me and van Treeck, Christoph},
  title     = {Time series aggregation for energy system design: review and extension of modelling seasonal storages},
  series = {Energy Informatics},
  volume    = {5},
  booktitle = {Energy Informatics},
  number    = {1, Article number: 17},
  editor    = {Weidlich, Anke and Neumann, Dirk and Gust, Gunther and Staudt, Philipp and Sch{\"a}fer, Mirko},
  publisher = {Springer Nature},
  issn      = {2520-8942},
  doi       = {10.1186/s42162-022-00208-5},
  pages     = {14 Seiten},
  year      = {2022},
  abstract  = {Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, "Time series aggregation for energy system design: Modeling seasonal storage", has developed a seasonal storage model to address this issue. Simultaneously, the paper "Optimal design of multi-energy systems with seasonal storage" has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.},
  language  = {en}
}
@inproceedings{PfaffSchmidtWilbringetal.2019,
  author    = {Pfaff, Raphael and Schmidt, Bernd and Wilbring, Daniela and Franzen, Julian},
  title     = {Wagon4.0 - the smart wagon for improved integration into Industry 4.0 plants},
  series = {Proceedings of the International Heavy Haul Association STS Conference 2019},
  booktitle = {Proceedings of the International Heavy Haul Association STS Conference 2019},
  pages     = {7 Seiten},
  year      = {2019},
  abstract  = {In many instances, freight vehicles exchange load or information with plants that are or will soon be Industry4.0 plants. The Wagon4.0 concept, as developed in close cooperation with e.g. port or mine operations, offers a maximum in railway operational efficiency while providing strong business cases already in the respective plant interaction. The Wagon4.0 consists of main components, a power supply, data network, sensors, actuators and an operating system, the so called WagonOS. The Wagon OS is implemented in a granular, self-sufficient manner, to allow basic features such as WiFi-Mesh and train christening in remote areas without network connection. Furthermore, the granularity of the operating system allows to extend the familiar app concept to freight rail rolling stock, making it possible to use specialised actuators for certain applications, e.g. an electrical parking brake or an auxiliary drive. In order to facilitate migration to the Wagon4.0 for existing fleets, a migration concept featuring five levels of technical adaptation was developed. The present paper investigates the benefits of Wagon4.0-implementations for the particular challenges of heavy haul operations by focusing on train christening, ep-assisted braking, autonomous last mile and traction boost operation as well as improved maintenance schedules},
  language  = {en}
}