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Development of a Volkswagen Jetta MK5 Hybrid Vehicle for Optimized System Efficiency Based on a Genetic Algorithm

Author

Listed:
  • Husam A. Neamah

    (Department of Electrical Engineering and Mechatronics, Faculty of Engineering, University of Debrecen, Òtemetö Utca 2-4, 4028 Debrecen, Hungary
    Technical Engineering College, Al-Ayen University, Thi-Qar 64001, Iraq)

  • Mohammed Dulaimi

    (College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq)

  • Alaa Silavinia

    (Department of Electrical Engineering and Mechatronics, Faculty of Engineering, University of Debrecen, Òtemetö Utca 2-4, 4028 Debrecen, Hungary)

  • Aminu Babangida

    (Department of Electrical Engineering and Mechatronics, Faculty of Engineering, University of Debrecen, Òtemetö Utca 2-4, 4028 Debrecen, Hungary)

  • Péter Tamás Szemes

    (Department of Electrical Engineering and Mechatronics, Faculty of Engineering, University of Debrecen, Òtemetö Utca 2-4, 4028 Debrecen, Hungary)

Abstract

Hybrid electric vehicles (HEVs) have emerged as a trendy technology for reducing over-dependence on fossil fuels and a global concern of gas emissions across transportation networks. This research aims to design the hybridized drivetrain of a Volkswagen (VW) Jetta MK5 vehicle on the basis of its mathematical background description and a computer-aided simulation (MATLAB/Simulink/Simscape, MATLAB R2023b). The conventional car operates through a five-speed manual gearbox, and a 2.0 TDI internal combustion engine (ICE) is first assessed. A comparative study evaluates the optimal fuel economy between the conventional and the hybrid versions based on a proportional-integral-derivative (PID) controller, whose optimal set-point is predicted and computed by a genetic algorithm (GA). For realistic hybridization, this research integrated a Parker electric motor and the diesel engine of a VW Crafter hybrid vehicle from the faculty of engineering to reduce fuel consumption and optimize the system performance of the proposed car. Moreover, a VCDS measurement unit is developed to collect vehicle data based on real-world driving scenarios. The simulation results are compared with experimental data to validate the model’s accuracy. The simulation results prove the effectiveness of the proposed energy management strategy (EMS), with an approximately 89.46% reduction in fuel consumption for the hybrid powertrain compared to the gas-powered traditional vehicle, and 90.05% energy efficiency is achieved.

Suggested Citation

  • Husam A. Neamah & Mohammed Dulaimi & Alaa Silavinia & Aminu Babangida & Péter Tamás Szemes, 2024. "Development of a Volkswagen Jetta MK5 Hybrid Vehicle for Optimized System Efficiency Based on a Genetic Algorithm," Energies, MDPI, vol. 17(5), pages 1-27, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1116-:d:1346354
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    References listed on IDEAS

    as
    1. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    2. Muhammad Maaruf & Waleed M. Hamanah & Mohammad A. Abido, 2023. "Hybrid Backstepping Control of a Quadrotor Using a Radial Basis Function Neural Network," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
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