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Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle

Author

Listed:
  • Aminu Babangida

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

  • Chiedozie Maduakolam Light Odazie

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

  • Péter Tamás Szemes

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

Abstract

In this article, a hybrid powertrain for the Volkswagen (VW) Crafter is designed using the Model-In-The-Loop (MIL) method. An enhanced Proportional-Integral (PI) control technique based on integral cost functions is developed by carrying out a time-based simulation in MATLAB/Simulink software to realize the optimal fuel economy of the vehicle. Moreover, a comparative study is conducted between the vehicle’s hybrid and pure electric versions to assess the optimal battery energy consumption per unit distance traveled. Communication within our vehicles’ Electronic Control Units (ECUs) is facilitated by a message-based protocol called a Controller Area Network (CAN). Consequently, this paper presents an online CAN Bus data analysis using the Hardware-In-The-Loop (HIL) method. This method uses a standard frame, J1939 CAN protocol, implemented with Net CAN Plus 110 hardware. A graphical user interface is developed on a host Personal Computer (PC) using LabVIEW for decoding the acquired raw CAN data to physical values. The simulation results reveal that the proposed controller is promising and suitable for realizing optimal performance over the HIL method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3436-:d:1212302
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    References listed on IDEAS

    as
    1. Yun Haitao & Zhao Yulan & Liu Zunnian & Hao Kui, 2013. "LQR-Based Power Train Control Method Design for Fuel Cell Hybrid Vehicle," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, November.
    2. Hu, Xiaosong & Zhang, Xiaoqian & Tang, Xiaolin & Lin, Xianke, 2020. "Model predictive control of hybrid electric vehicles for fuel economy, emission reductions, and inter-vehicle safety in car-following scenarios," Energy, Elsevier, vol. 196(C).
    3. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 238(PC).
    4. Hwa-seon Kim & Seong-jin Jang & Jong-wook Jang, 2015. "A Study on Development of Engine Fault Diagnostic System," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-6, July.
    5. Cipek, Mihael & Pavković, Danijel & Petrić, Joško, 2013. "A control-oriented simulation model of a power-split hybrid electric vehicle," Applied Energy, Elsevier, vol. 101(C), pages 121-133.
    6. Zhang, Junjiang & Yang, Yang & Hu, Minghui & Yang, Zhong & Fu, Chunyun, 2021. "Longitudinal–vertical comprehensive control for four-wheel drive pure electric vehicle considering energy recovery and ride comfort," Energy, Elsevier, vol. 236(C).
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