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EXPert: A driver assistance system for fuel efficient driving

: Guan, Tianyi; Frey, Christian

Postprint urn:nbn:de:0011-n-2020002 (338 KByte PDF)
MD5 Fingerprint: cf79e38b74f023566fe7815aab620792
Erstellt am: 3.5.2012

Schwieger, V. ; Univ. Stuttgart; Univ. Hohenheim:
3rd International Conference on Machine Control & Guidance, MCG 2012. Proceedings : Stuttgart, 27th - 29th March 2012
Stuttgart: Universität Stuttgart, 2012
ISBN: 978-3-00-037295-7
11 S.
International Conference on Machine Control & Guidance (MCG) <3, 2012, Stuttgart>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

With ever rising resource prices, simultaneous expansion of trade and transportation as well as increased awareness of environmental protection, it is important to reduce the fuel consumption of trucks, transporters, agricultural vehicles and other heavy-duty vehicles. While new generations of vehicles like electric cars are still in in their infancy, it is already possible to reduce fuel consumption through fuel economic driving behavior. The EU funded project "EXPERT" (EXPert System for a more Efficient Road Transportation), a consortium of European partners including Fraunhofer IOSB, is investigating ways to support the truck driver to accomplish this goal. The main focus of this paper is on the "Driving Efficiency Module", created by Fraunhofer IOSB, within EXPERT system which uses an adaptive vehicle model to generate fuel efficient online guidelines for the driver. An inverse vehicle model and a partial power train model formulation are presented and compared with each other. System identification methods, solely using CAN-Bus data, are proposed to estimate the unknown model parameters that will enable the model to adapt to a wide range of different vehicles. The adaptive model is then used in an optimization routine that generates fuel efficient guidelines depending on the current vehicle and the current state. Aided by the fuel efficient guidelines, the driver will be able to adopt a more fuel efficient driving behavior. This paper concludes with an evaluation of the proposed ideas based on real world data and simulated data.