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Predictive energy efficiency optimization of an electric vehicle using information about traffic light sequences and other vehicles

: Guan, Tianyi; Frey, Christian W.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016 : November 1-4, 2016, Rio de Janeiro, Brazil; Proceedings
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2016
ISBN: 978-1-5090-1889-5
ISBN: 978-1-5090-1888-8
ISBN: 978-1-5090-1890-1 (Print)
International Conference on Intelligent Transportation Systems (ITSC) <19, 2016, Rio de Janeiro>
Fraunhofer IOSB ()
dynamic obstacle avoidance; dynamic programming; electric vehicle; energy efficient driving; lane change; model predictive optimization; Search space reduction; traffic lights

Major obstacles for electric vehicles are the relatively short range and insufficient infrastructure to sustain long travels among other challenges. While batteries and other technologies, that enable future vehicles to overcome the difficulties, are in development, it is already possible to decrease energy consumption by applying a more energy efficient driving behavior. Furthermore, the rise of advanced perception and V2X technologies have opened up new possibilities for safety, but also energy efficiency applications. This publication proposes a model predictive optimization approach that makes use of a power-Train model, sequences of traffic lights and other vehicles to compute energy efficient velocity and gear shift profiles over a finite optimization horizon. A stagewise forward-backward Dynamic Programming approach is used for optimization. In order to decrease the search space, the optimization works with alternating state components among other techniques. We will also introduce the REM 2030 electric vehicle that our project partners have developed in the project REM 2030