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Predictive energy efficiency optimization of an electric vehicle using traffic light sequence information

: Guan, Tianyi; Frey, Christian


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Intelligent Transportation Systems Society -ITSS-:
IEEE International Conference on Vehicular Electronics and Safety, ICVES 2016. Proceedings : July 10-12, 2016, Beijing, China
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-2934-1
ISBN: 978-1-5090-2933-4
ISBN: 978-1-5090-2932-7
International Conference on Vehicular Electronics and Safety (ICVES) <2016, Beijing>
Fraunhofer IOSB ()

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 the energy consumption by applying an energy efficient driving behavior. Furthermore, the rise of V2X technologies have opened up new possibilities for safety and energy efficiency applications. This publication proposes a model predictive approach that makes use of a power-train model and a sequence of traffic lights over a finite optimization horizon. The optimization problem is solved in a unified manner, i.e. power-train properties and traffic light phases are not considered separately but evaluated in a single cost function. 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. We will also introduce the REM 2030 electric vehicle that our project partners have developed in the project REM 2030.