Refine
Year of publication
- 2019 (44) (remove)
Document Type
- Conference Proceeding (24)
- Article (9)
- Part of a Book (9)
- Book (2)
Keywords
- Digitalisierung (2)
- Enterprise Architecture (2)
- Robotic Process Automation (2)
- Advanced driver assistance systems (ADAS/AD) (1)
- Arbeit 4.0 (1)
- BEV (1)
- Case Study (1)
- Chatbots (1)
- Digital Age (1)
- Engineering optimization (1)
- Forschung (1)
- Forschungsinformationssystem (1)
- Forschungsprozess (1)
- Gearbox (1)
- Graph Theory (1)
- ISO 26262 (1)
- Machine learning (1)
- Mixed-integer nonlinear black-box optimization (1)
- Optimization (1)
- Powertrain (1)
Institute
- Fachbereich Elektrotechnik und Informationstechnik (44) (remove)
In this paper, an approach to propulsion system modelling for hybrid-electric general aviation aircraft is presented. Because the focus is on general aviation aircraft, only combinations of electric motors and reciprocating combustion engines are explored. Gas turbine hybrids will not be considered. The level of the component's models is appropriate for the conceptual design stage. They are simple and adaptable, so that a wide range of designs with morphologically different propulsive system architectures can be quickly compared. Modelling strategies for both mass and efficiency of each part of the propulsion system (engine, motor, battery and propeller) will be presented.
Embedded Systems für IoT
(2019)
In diesem Paper wird die Entwicklung und Evaluation eines grafischen Regeleditors für das Erstellen von „Smart Living Environments“-Services vorgestellt. Dafür werden zunächst die Deduktion und Implementierung des grafischen Regeleditors erläutert. Anschließend wird eine Probandenstudie vorgestellt, in welcher der Mehrwert bezogen auf die Aspekte Zeit, Fehleranfälligkeit und Gebrauchstauglichkeit festgestellt wird.
In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.