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Partner: Daimler AG

Predictive ECO Driving Strategy for EV/HEV

Development of energy-optimized driving strategies for electric vehicles using adaptive longitudinal dynamic driver models.
[Photo: DAIMLER AG]

Description

Efficiency and Sustainability have become central areas of research in automotive industry in recent years. Electric and Hybrid Electric Vehicles (EV/HEV) can account for everstricter statutory requirements governing emissions and fuel consumption. Together with an "Advanced Driver Assistance System" (ADAS), providing road data of the upcoming section, the full potential of these alternative and hybrid powertrains can be unfolded.

ADAS road data on the upcoming section is used to calculate optimal velocity trajectories for electric and hybrid electric vehicles, as well as gear and torque-split trajectories for HEV. A complex simulation environment considering longitudinal vehicle dynamics was established in previous projects. Also, trajectory planning was implemented using an online-capable local optimization algorithm. Results were compared with global optimal solutions of an offline dynamic programming algorithm.

Due to high computational effort and non-deterministic behavior of optimization algorithms, their application in series vehicles is undesirable. Therefore, this project focusses on the development of intelligent predictive heuristic approaches to generate near optimal trajectories based on the results of optimization results. Calculated trajectories shall be communicated to the driver in the form of driving suggestions.

The second main subject of this project consists in the development of an adaptive driver model, detecting longitudinal driver characteristics on the basis of the driver's reactions to these driving suggestions. The identified  longitudinal driver.

Coordinator

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M.Sc.

Florian Morlock

Research Assistant