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Unified predictive fuel efficiency optimization using traffic light sequence information

By Tianyi Guan and Christian W. Frey

Abstract

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

Topics: dynamic programming, reuse historic costs, Search space reduction, fuel efficiency driving, model predictive optimization
Year: 2016
DOI identifier: 10.1109/IVS.2016.7535527
OAI identifier: oai:fraunhofer.de:N-426381
Provided by: Fraunhofer-ePrints
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