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Extending Behavioral Models to Generate Mission-Based Driving Cycles for Data-Driven Vehicle Development
Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering. Swedish Natl Rd and Transport Res Inst, Sweden.
Linköping University, Department of Electrical Engineering. Linköping University, Faculty of Science & Engineering. Saab Def and Space, S-58015 Linkoping, Sweden.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
2019 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 68, no 2, p. 1222-1230Article in journal (Refereed) Published
Abstract [en]

Driving cycles are nowadays, to an increasing extent, used as input to model-based vehicle design and as training data for development of vehicle models and functions with machine learning algorithms. Recorded real driving data may underrepresent or even lack important characteristics, and therefore there is a need to complement driving cycles obtained from real driving data with synthetic data that exhibit various desired characteristics. In this paper, an efficient method for generation of mission-based driving cycles is developed for this purpose. It is based on available effective methods for traffic simulation and available maps to define driving missions. By comparing the traffic simulation results with real driving data, insufficiencies in the existing behavioral model in the utilized traffic simulation tool are identified. Based on these findings, four extensions to the behavioral model are suggested, staying within the same class of computational complexity so that it can still be used in a large scale. The evaluation results show significant improvements in the match between the data measured on the road and the outputs of the traffic simulation with the suggested extensions of the behavioral model. The achieved improvements can be observed with both visual inspection and objective measures. For instance, the 40% difference in the relative positive acceleration of the originally simulated driving cycle compared to real driving data was eliminated using the suggested model.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 68, no 2, p. 1222-1230
Keywords [en]
Powertrain dimensioning; representative driving cycles; real driving data; driver model; machine learning
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-155012DOI: 10.1109/TVT.2018.2887031ISI: 000458803200015OAI: oai:DiVA.org:liu-155012DiVA, id: diva2:1297517
Note

Funding Agencies|Swedish Electro-mobility center

Available from: 2019-03-20 Created: 2019-03-20 Last updated: 2019-08-23

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Kharrazi, SogolAlmen, MarcusFrisk, ErikNielsen, Lars
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