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Delivery vehicle fleet data collection, analysis and naturalistic driving cycles synthesis

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  • Branimir Å kugor
  • JoÅ¡ko Deur

Abstract

The paper deals with experimental characterisation and analysis of an isolated delivery vehicle fleet system of a retail company. The on-vehicle data collection has been conducted for a fleet of 10 delivery vehicles running continuously over a three month period. Next, a wide statistical analysis of the collected data is presented, in order to provide a basis for future investigation of possible benefits of replacing the conventional vehicle fleet with a hypothetical one based on electric vehicles. Finally, the recorded large set of driving cycles is used for the purpose of stochastic-based synthesis and validation of a small number of representative driving cycles. Such naturalistic driving cycles can further be used for electric vehicle configuration optimisation and controller design, as well as for design and verification of a vehicle fleet model aimed for various energy planning and smart charging studies.

Suggested Citation

  • Branimir Å kugor & JoÅ¡ko Deur, 2016. "Delivery vehicle fleet data collection, analysis and naturalistic driving cycles synthesis," International Journal of Innovation and Sustainable Development, Inderscience Enterprises Ltd, vol. 10(1), pages 19-39.
    • Handle: RePEc:ids:ijisde:v:10:y:2016:i:1:p:19-39
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    References listed on IDEAS

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    1. Lund, Henrik & Kempton, Willett, 2008. "Integration of renewable energy into the transport and electricity sectors through V2G," Energy Policy, Elsevier, vol. 36(9), pages 3578-3587, September.
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    Cited by:

    1. Zvonimir Dabčević & Branimir Škugor & Jakov Topić & Joško Deur, 2022. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology," Energies, MDPI, vol. 15(11), pages 1-21, June.
    2. Jakov Topić & Branimir Škugor & Joško Deur, 2021. "Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    3. Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
    4. Škugor, Branimir & Deur, Joško, 2016. "A bi-level optimisation framework for electric vehicle fleet charging management," Applied Energy, Elsevier, vol. 184(C), pages 1332-1342.

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