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Unified predictive fuel efficiency optimization using traffic light sequence information

: Guan, Tianyi; Frey, Christian W.

Postprint urn:nbn:de:0011-n-4263814 (321 KByte PDF)
MD5 Fingerprint: 42fcf1c9f81cbb93b48c3ca68de75418
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Erstellt am: 21.12.2016

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Intelligent Transportation Systems Society -ITSS-:
IEEE Intelligent Vehicles Symposium, IV 2016 : June 19-22, 2016, Gothenburg, Sweden
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-1822-2
ISBN: 978-1-5090-1821-5
Intelligent Vehicles Symposium (IV) <2016, Gothenburg>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
dynamic programming; reuse historic costs; Search space reduction; fuel efficiency driving; model predictive optimization

Energy efficiency has become a major issue in trade, transportation and environment protection. While the next generation of zero emission propulsion systems still have difficulties in reaching similar travel distances as power-trains with combustion engines, it is already possible to increase fuel efficiency in regular vehicles by applying a more fuel efficient driving behavior. 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 involving cost reutilization is used for optimization. In order to further decrease the search space, certain continuous entities are not explicitly regarded as a state component, but rather calculated during optimization.