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Development and Fuel Economy Optimization of Series–Parallel Hybrid Powertrain for Van-Style VW Crafter Vehicle

Author

Listed:
  • Ahmed Nabil Farouk Abdelbaky

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

  • Aminu Babangida

    (Department of Vehicles Engineering, Institute of Vehicles and Mechatronics, Faculty of Engineering, University of Debrecen, Òtemetö Utca 2-4, 4028 Debrecen, Hungary
    Department of Electrical Engineering, Faculty of Engineering, Aliko Dangote University of Science and Technology, Wudil 713101, Nigeria)

  • Abdullahi Bala Kunya

    (Department of Electrical, Telecommunications and Computer Engineering, Kampala International University, Western Campus, Ishaka P.O. Box 20000, Uganda
    Department of Electrical Engineering, Ahmadu Bello University, Zaria 810006, Nigeria)

  • Péter Tamás Szemes

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

Abstract

The presence of toxic gas emissions from conventional vehicles is worrisome globally. Over the past few years, there has been a broad adoption of electric vehicles (EVs) to reduce energy usage and mitigate environmental emissions. The EVs are characterized by limited range, cost, and short range. This prompts the need for hybrid electric vehicles (HEVs). This study describes the conversion of a 2022 Volkswagen Crafter (VW) 35 TDI 340 delivery van from a conventional diesel powertrain into a hybrid electric vehicle (HEV) augmented with synchronous electrical machines (motor and generator) and a BMW i3 60 Ah battery pack. A downsized 1.5 L diesel engine and an electric motor–generator unit are integrated via a planetary power split device supported by a high-voltage lithium-ion battery. A MATLAB (R2024b) Simulink model of the hybrid system is developed, and its speed tracking PID controller is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) methods. The simulation results show significant efficiency gains: for example, average fuel consumption falls from 9.952 to 7.014 L/100 km (a 29.5% saving) and CO 2 emissions drop from 260.8 to 186.0 g/km (a 74.8 g reduction), while the vehicle range on a 75 L tank grows by ~40.7% (from 785.7 to 1105.5 km). The optimized series–parallel powertrain design significantly improves urban driving economy and reduces emissions without compromising performance.

Suggested Citation

  • Ahmed Nabil Farouk Abdelbaky & Aminu Babangida & Abdullahi Bala Kunya & Péter Tamás Szemes, 2025. "Development and Fuel Economy Optimization of Series–Parallel Hybrid Powertrain for Van-Style VW Crafter Vehicle," Energies, MDPI, vol. 18(14), pages 1-42, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3688-:d:1700469
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    References listed on IDEAS

    as
    1. Roberto H. Q. Filho & Rodrigo P. M. Ruiz & Eisenhawer de M. Fernandes & Rosalvo B. Filho & Felipe C. Pimenta, 2024. "Development of a Genetic Algorithm-Based Control Strategy for Fuel Consumption Optimization in a Mild Hybrid Electrified Vehicle’s Electrified Propulsion System," Energies, MDPI, vol. 17(9), pages 1-19, April.
    2. Pierpaolo Polverino & Ivan Arsie & Cesare Pianese, 2021. "Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon," Energies, MDPI, vol. 14(12), pages 1-11, June.
    3. ByungHoon Yang & KyoungJoo Kim & HyungSoo Mok, 2020. "Fast and Robust Hybrid Starter and Generator Speed Control for Improving Drivability of Parallel Hybrid Electric Vehicles," Energies, MDPI, vol. 13(19), pages 1-21, September.
    4. Aminu Babangida & Péter Tamás Szemes, 2024. "Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain," Energies, MDPI, vol. 17(18), pages 1-38, September.
    5. Jia, Chunchun & Liu, Wei & He, Hongwen & Chau, K.T., 2025. "Superior energy management for fuel cell vehicles guided by improved DDPG algorithm: Integrating driving intention speed prediction and health-aware control," Applied Energy, Elsevier, vol. 394(C).
    6. 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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