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The Economic Aspect of Using Different Plug-In Hybrid Driving Techniques in Urban Conditions

Author

Listed:
  • Piotr Wróblewski

    (Division of Aircraft Construction and Operation, Faculty of Mechatronics, Institute of Aviation Technology, Armament and Aerospace of the Military University of Technology, Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland)

  • Jerzy Kupiec

    (Faculty of Civil and Transport Engineering, Institute of Machines and Motor Vehicles, Poznan University of Technology, Piotrowo Street 3, 60-965 Poznan, Poland)

  • Wojciech Drożdż

    (Research Center for Management of Energy Sector, Institute of Management, University of Szczecin, Cukrowa Street 8, 71-004 Szczecin, Poland)

  • Wojciech Lewicki

    (Faculty of Economics, West Pomeranian University of Technology Szczecin, Zołnierska 47, 71-210 Szczecin, Poland)

  • Jarosław Jaworski

    (Research Center for Management of Energy Sector, Institute of Management, University of Szczecin, Cukrowa Street 8, 71-004 Szczecin, Poland)

Abstract

Plug-in hybrids (PHEV) have become popular due to zero-emission driving, e.g., in urban areas, and using an internal combustion engine on longer distances. Energy consumption by the PHEV depends on many factors which can be either dependent or independent of the driver. The article examines how the driver can use the vehicle’s capabilities to influence its wear. Determining the optimal driving technique, due to the adopted nature of the timetable, is the basic variable that determines the profitability of using a given drive system. Four driving techniques have been selected to determine which one can offer the largest advantages. A vehicle-dedicated application has recorded the drivetrain performance on a predetermined route through an urban area. The analysis of results has demonstrated which of the driving techniques provides measurable effects in terms of reduced energy consumption and the shortest travelling time. The study shows longitudinal acceleration and torque generated by the electric drive. The information included in the study can help any PHEV user reduce the operating cost by applying an appropriate driving technique. The proposed research introduces the possibilities of assessing the influence of the driving style on energy consumption. The innovative side of this research is the observation of stochastic phenomena that are difficult to detect when using approximation modelling.

Suggested Citation

  • Piotr Wróblewski & Jerzy Kupiec & Wojciech Drożdż & Wojciech Lewicki & Jarosław Jaworski, 2021. "The Economic Aspect of Using Different Plug-In Hybrid Driving Techniques in Urban Conditions," Energies, MDPI, vol. 14(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3543-:d:574840
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    References listed on IDEAS

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    Cited by:

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    2. Trieu Minh Vu & Reza Moezzi & Jindrich Cyrus & Jaroslav Hlava & Michal Petru, 2021. "Automatic Clutch Engagement Control for Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 14(21), pages 1-15, November.
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    5. Narongkorn Uthathip & Pornrapeepat Bhasaputra & Woraratana Pattaraprakorn, 2021. "Stochastic Modelling to Analyze the Impact of Electric Vehicle Penetration in Thailand," Energies, MDPI, vol. 14(16), pages 1-23, August.
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    7. Konstantina Anastasiadou & Nikolaos Gavanas & Magda Pitsiava-Latinopoulou & Evangelos Bekiaris, 2021. "Infrastructure Planning for Autonomous Electric Vehicles, Integrating Safety and Sustainability Aspects: A Multi-Criteria Analysis Approach," Energies, MDPI, vol. 14(17), pages 1-19, August.
    8. Tobias Frambach & Ralf Kleisch & Ralf Liedtke & Jochen Schwarzer & Egbert Figgemeier, 2022. "Environmental Impact Assessment and Classification of 48 V Plug-in Hybrids with Real-Driving Use Case Simulations," Energies, MDPI, vol. 15(7), pages 1-21, March.
    9. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
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