Ziel der Arbeit ist eine ökologische und ökonomische Bewertung elektrifizierter Antriebsstrangkonzepte, um auf Basis dieser Modelle Entwicklungsziele abzuleiten.

The ramp-up of e-mobility brings with it challenges for integration into the electricity grid. To test grid-friendly charging, an intelligent charging station is to be developed at the IMS.

Shared test platforms can realize multi-functionality investigations at the early stage of the vehicle powertrain development.

At IMS, the CONNECT (powertrain test bench) and Driveception (driving simulator) are integrated for a distributed measurement of powertrain dynamics and their impact on subjective perception during the launch process. The student work will be focused on the realization of the test bench networking and its real-time performance for the launch process study.

At the IMS, the potential for increasing the efficiency of powertrains by knowing the speed to be expected in the next few seconds is being investigated on the basis of AI-based predictions.

The Kolmogorov-Arnold networks (KAN) approach presented in April 2024 promises faster training, improved accuracy and interpretability compared to conventional multi-layer perceptrons (MLP). So far, only a simple Matlab implementation of KANs with basic functionality exists.

The aim of this advertised work is to realise a KAN implementation against the background of the Matlab Deep Learning Toolbox and to use it for speed prediction.

A data set comprising over 29,000 kilometres is available as a data basis.