@inproceedings{05f3d6532a5d449e9bd8658606f49b73,
title = "Derivation of a real-life driving cycle from fleet testing data with the Markov-Chain-Monte-Carlo Method",
abstract = "Future driving cycles are subject to a number of regulations and requirements. A vehicles ability to meet the emission regulations under real-life conditions is based on a precise testing procedure. Additionally, intelligent vehicle design needs to be customer oriented. The requirements for an optimum drivetrain design have to be deviated from the customers driving behavior. Especially in the price sensitive long-haul business. In a new approach the Markov-Chain Method (MC) is applied to fleet testing data from the research project Truck2030. Two different transportation companies collected 95,279 km in long-haul traffic. The objective is to find a shortened driving cycle with the quality to represent the original fleet testing data. The designed MC is focused on topographic and dynamic information of the dataset. The results show a discrepancy below 1 \% in fuel consumption error between the original fleet testing data and the representative driving cycle.",
keywords = "computational intelligence, fleet-testing, intelligent logistics, markov-chain, real-life driving cycles, vehicle design, vehicle-emissions",
author = "Michael Fries and Alexandre Baum and Michael Wittmann and Markus Lienkamp",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 ; Conference date: 04-11-2018 Through 07-11-2018",
year = "2018",
month = dec,
day = "7",
doi = "10.1109/ITSC.2018.8569547",
language = "English",
series = "IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2550--2555",
booktitle = "2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018",
}