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Adaptive Driving Cycles of EVs for Reducing Energy Consumption

Author

Listed:
  • Iwona Komorska

    (Department of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, Malczewskiego 29, 26-600 Radom, Poland)

  • Andrzej Puchalski

    (Department of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, Malczewskiego 29, 26-600 Radom, Poland)

  • Andrzej Niewczas

    (Motor Transport Institute, Jagiellońska 80, 03-301 Warszawa, Poland)

  • Marcin Ślęzak

    (Motor Transport Institute, Jagiellońska 80, 03-301 Warszawa, Poland)

  • Tomasz Szczepański

    (Motor Transport Institute, Jagiellońska 80, 03-301 Warszawa, Poland)

Abstract

A driving cycle is a time series of a vehicle’s speed, reflecting its movement in real road conditions. In addition to certification and comparative research, driving cycles are used in the virtual design of drive systems and embedded control algorithms, traffic management and intelligent road transport (traffic engineering). This study aimed to develop an adaptive driving cycle for a known route to optimize the energy consumption of an electric vehicle and improve the driving range. A novel distance-based adaptive driving cycle method was developed. The proposed algorithm uses the segmentation and iterative synthesis procedures of Markov chains. Energy consumption during driving is monitored on an ongoing basis using Gaussian process regression, and speed and acceleration are corrected adaptively to maintain the planned energy consumption. This paper presents the results of studies of simulated driving cycles and the performance of the algorithm when applied to the real recorded driving cycles of an electric vehicle.

Suggested Citation

  • Iwona Komorska & Andrzej Puchalski & Andrzej Niewczas & Marcin Ślęzak & Tomasz Szczepański, 2021. "Adaptive Driving Cycles of EVs for Reducing Energy Consumption," Energies, MDPI, vol. 14(9), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2592-:d:547742
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    References listed on IDEAS

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    1. Chen, Zeyu & Zhang, Qing & Lu, Jiahuan & Bi, Jiangman, 2019. "Optimization-based method to develop practical driving cycle for application in electric vehicle power management: A case study in Shenyang, China," Energy, Elsevier, vol. 186(C).
    2. Yuan, Xinmei & Zhang, Chuanpu & Hong, Guokai & Huang, Xueqi & Li, Lili, 2017. "Method for evaluating the real-world driving energy consumptions of electric vehicles," Energy, Elsevier, vol. 141(C), pages 1955-1968.
    3. Jie Lin & Debbie A. Niemeier, 2003. "Estimating Regional Air Quality Vehicle Emission Inventories: Constructing Robust Driving Cycles," Transportation Science, INFORMS, vol. 37(3), pages 330-346, August.
    4. Brady, John & O’Mahony, Margaret, 2016. "Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas," Applied Energy, Elsevier, vol. 177(C), pages 165-178.
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    Cited by:

    1. Aaron Shmaryahu & Nissim Amar & Alexander Ivanov & Ilan Aharon, 2021. "Sizing Procedure for System Hybridization Based on Experimental Source Modeling for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-21, August.
    2. Laura Tribioli & Manfredi Villani, 2022. "Electrified Powertrains for a Sustainable Mobility: Topologies, Design and Integrated Energy Management Strategies," Energies, MDPI, vol. 15(9), pages 1-2, April.
    3. Zhecheng Jing & Tianxiao Wang & Shupei Zhang & Guolin Wang, 2022. "Development Method for the Driving Cycle of Electric Vehicles," Energies, MDPI, vol. 15(22), pages 1-12, November.
    4. Štefan Bojnec & Alan Križaj, 2021. "Electricity Markets during the Liberalization: The Case of a European Union Country," Energies, MDPI, vol. 14(14), pages 1-21, July.
    5. Tomáš Settey & Jozef Gnap & František Synák & Tomáš Skrúcaný & Marek Dočkalik, 2021. "Research into the Impacts of Driving Cycles and Load Weight on the Operation of a Light Commercial Electric Vehicle," Sustainability, MDPI, vol. 13(24), pages 1-25, December.

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