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Modeling of Working Machines Synergy in the Process of the Hybrid Electric Vehicle Acceleration

Author

Listed:
  • Konrad Prajwowski

    (Department of Automotive Engineering, Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology, Piastow Avenue 19, 70-310 Szczecin, Poland)

  • Wawrzyniec Golebiewski

    (Department of Automotive Engineering, Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology, Piastow Avenue 19, 70-310 Szczecin, Poland)

  • Maciej Lisowski

    (Department of Automotive Engineering, Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology, Piastow Avenue 19, 70-310 Szczecin, Poland)

  • Karol F. Abramek

    (Department of Automotive Engineering, Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology, Piastow Avenue 19, 70-310 Szczecin, Poland)

  • Dominik Galdynski

    (Department of Automotive Engineering, Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology, Piastow Avenue 19, 70-310 Szczecin, Poland)

Abstract

There are many different mathematical models that can be used to describe relations between energy machines in the power-split hybrid drive system. Usually, they are created based on simulations or measurements in bench (laboratory) conditions. In that sense, however, these are the idealized conditions. It is not known how the internal combustion engine and electrical machines work in real road conditions, especially during acceleration. This motivated the authors to set the goal of solving this research problem. The solution was to implement and develop the model predictive control (MPC) method for driving modes (electric, normal) of a hybrid electric vehicle equipped with a power-split drive system. According to the adopted mathematical model, after determining the type of model and its structure, the measurements were performed. There were carried out as road tests in two driving modes of the hybrid electric vehicle: electric and normal. The measurements focused on the internal combustion engine and electrical machines parameters (torque, rotational speed and power), state of charge of electrochemical accumulator system and equivalent fuel consumption (expressed as a cost function). The operating parameters of the internal combustion engine and electric machines during hybrid electric vehicle acceleration assume the maximum values in the entire range (corresponding to the set vehicle speeds). The process of the hybrid electric vehicle acceleration from 0 to 47 km/h in the electric mode lasted for 12 s and was transferred into the equivalent fuel consumption value of 5.03 g. The acceleration of the hybrid electric vehicle from 0 to 47 km/h in the normal mode lasted 4.5 s and was transferred to the value of 4.23 g. The hybrid electric vehicle acceleration from 0 to 90 km/h in the normal mode lasted 11 s and corresponded to the cost function value of 26.43 g. The presented results show how the fundamental importance of the hybrid electric vehicle acceleration process with a fully depressed gas pedal is (in these conditions the selected driving mode is a little importance).

Suggested Citation

  • Konrad Prajwowski & Wawrzyniec Golebiewski & Maciej Lisowski & Karol F. Abramek & Dominik Galdynski, 2020. "Modeling of Working Machines Synergy in the Process of the Hybrid Electric Vehicle Acceleration," Energies, MDPI, vol. 13(21), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5818-:d:441152
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    References listed on IDEAS

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    1. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
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    3. Chen, Syuan-Yi & Hung, Yi-Hsuan & Wu, Chien-Hsun & Huang, Siang-Ting, 2015. "Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization," Applied Energy, Elsevier, vol. 160(C), pages 132-145.
    4. Hsiu-Ying Hwang & Jia-Shiun Chen, 2020. "Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization," Energies, MDPI, vol. 13(9), pages 1-18, May.
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    Cited by:

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