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Description
Electromobility is an important step towards climate-neutral and locally emission-free mobility. A key challenge of electric mobility is the battery-electric range. Compared to conventional vehicles, this can limit customers' mobility in everyday life and on special journeys. Precise consumption forecasts and personalised travel and route planning assistants are needed to keep these restrictions to a minimum. To estimate real consumption, driver and vehicle influences, as well as route and current traffic information, must be taken into account.
Project objective
The energy consumption forecast is route- and vehicle-specific. This includes the driving resistance equation, as well as the losses of the battery and the drivetrain, the energy consumption of auxiliary consumers such as air conditioning, radio, entertainment systems and the conditioning of the battery or the entire drivetrain. As part of this doctorate, various approaches for improving range prediction and the consumption model are to be developed, analysed and evaluated.
The aim is to extend the existing consumption model with an adaptive, learning sub-module for monitoring disturbance variables in order to achieve a more precise and automated parameterisation of the overall model. The sub-module should take into account uncertainties caused by external influences and include calibration and adaptation during driving as well as over longer time horizons.
Interested?
If you have questions regarding the project or in doing a thesis or a student assistant job, please do not hesitate to contact us.
Contact
Sören Hain
M. Sc.Research Assistant