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Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain

Author

Listed:
  • Aminu Babangida

    (Department of Vehicles Engineering, Vehicles and Mechatronics Institute, Faculty of Engineering, University of Debrecen, Ótemető u. 2-4, 4028 Debrecen, Hungary
    These authors contributed equally to this work.)

  • Péter Tamás Szemes

    (Department of Vehicles Engineering, Vehicles and Mechatronics Institute, Faculty of Engineering, University of Debrecen, Ótemető u. 2-4, 4028 Debrecen, Hungary
    These authors contributed equally to this work.)

Abstract

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle’s hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO 2 emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO 2 emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO 2 /km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4721-:d:1483049
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    References listed on IDEAS

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    1. Qian Zhang & Shaopeng Tian, 2023. "Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    2. Aminu Babangida & Chiedozie Maduakolam Light Odazie & Péter Tamás Szemes, 2023. "Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle," Mathematics, MDPI, vol. 11(15), pages 1-19, August.
    3. 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.
    4. Nour A. Mohamed & Hany M. Hasanien & Abdulaziz Alkuhayli & Tlenshiyeva Akmaral & Francisco Jurado & Ahmed O. Badr, 2023. "Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
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