Hy-Nets
Efficient hybrid powertrains by vehicle communication
The objective of the Hy-Nets project is to develop a novel approach for improving the resource and energy efficiency of connected hybrid cars: hybrid propulsions on a test bench will be coupled with an environmental simulation of the vehicles as well as of the communication between the connected cars. This way, new insights can be derived of their influence in realistic city environments. We plan using the city of Paderborn as an example of a typical European city to assess the potentials of hybrid propulsion in combination with networked cars.
During the project meeting on November 8th, 2016, Max Tacke presented his master's thesis:
Integration of geodata into an environment simulation for the testing of predictive and automated driving functions (Einbindung von Geodaten in eine Umfeldsimulation zum Testen von prädiktiven und automatisierten Fahrfunktionen)
Concerning the rising complexity in the area of driver assistant systems, it is necessary to reproducibly test newly developed functions in a realistic car environment as early as possible. The construction of the geo-specific environment simulation, which is needed for this purpose, is a complex process, during which the integration of geo-data is mostly done by hand by the developers. Due to the complexity of the driver assistant systems, the needed test depth adds to the problem.
Therefore, as a first step, the software tools, which are needed for an automatized modelling of a simulation environment, were expanded within this Master’s Thesis and in cooperation with the company dSpace. Thereafter, a reference route was defined for relevant geo-specific features, such as road routing with an elevation profile and traffic lights. It was extracted from databases and integrated into the simulation configuration. In a next step, reference measurements for this route were recorded with a demonstrator vehicle. These vehicle and sensor data were integrated into the simulation to depict other road users. Finally, the process to generate the simulation environment was validated, and a predicative and automatized driving function was developed and examined to increase the energy efficiency of the Ego-vehicle. Thus, the execution of realistic tests enables an objective evaluation of predictive and automatized driving functions.
Supervisors: Markus Eisenbarth M. Sc. and Dipl.-Ing. Thorsten Plum, Correctors: Prof. Dr.-Ing Jakob Andert and Dr.-Ing. Marco Günther
Project partners