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Fuzzy-Energy-Management-Based Intelligent Direct Torque Control for a Battery—Supercapacitor Electric Vehicle

Author

Listed:
  • Adel Oubelaid

    (Laboratoire de Technologie Industrielle et de l’Information, Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria)

  • Hisham Alharbi

    (Department of Electrical Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Abdullah S. Bin Humayd

    (Department of Electrical Engineering, Umm Al-Qura University, Makkah 21421, Saudi Arabia)

  • Nabil Taib

    (Laboratoire de Technologie Industrielle et de l’Information, Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria)

  • Toufik Rekioua

    (Laboratoire de Technologie Industrielle et de l’Information, Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria)

  • Sherif S. M. Ghoneim

    (Department of Electrical Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

This paper presents a proposed fuzzy energy management strategy developed for a battery−super capacitor electric vehicle. In addition to providing different driving modes, the proposed strategy delivers the suitable type and amount of power to the vehicle. Furthermore, the proposed strategy takes into account possible failures in vehicle power sources. The speed and torque of the HEV traction machine are simultaneously controlled using a genetic algorithm that provides simultaneous tuning via the use of newly proposed cost functions that give the designer the ability to tradeoff and prioritize between the design variables to be minimized. The simulation results show that the intelligent speed and torque control and the fuzzy power management strategy improved the vehicle’s performance in terms of ripple minimization. The real-time simulation is conducted using the RT LAB simulator, and the results obtained correspond to those obtained in the numerical simulation using MATLAB/Simulink.

Suggested Citation

  • Adel Oubelaid & Hisham Alharbi & Abdullah S. Bin Humayd & Nabil Taib & Toufik Rekioua & Sherif S. M. Ghoneim, 2022. "Fuzzy-Energy-Management-Based Intelligent Direct Torque Control for a Battery—Supercapacitor Electric Vehicle," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8407-:d:858924
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    References listed on IDEAS

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    1. Alaa A. Zaky & Ahmed Fathy & Hegazy Rezk & Konstantina Gkini & Polycarpos Falaras & Amlak Abaza, 2021. "A Modified Triple-Diode Model Parameters Identification for Perovskite Solar Cells via Nature-Inspired Search Optimization Algorithms," Sustainability, MDPI, vol. 13(23), pages 1-22, November.
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    Cited by:

    1. Hartani, Mohamed Amine & Rezk, Hegazy & Benhammou, Aissa & Hamouda, Messaoud & Abdelkhalek, Othmane & Mekhilef, Saad & Olabi, A.G., 2023. "Proposed frequency decoupling-based fuzzy logic control for power allocation and state-of-charge recovery of hybrid energy storage systems adopting multi-level energy management for multi-DC-microgrid," Energy, Elsevier, vol. 278(C).
    2. Khoudir Kakouche & Adel Oubelaid & Smail Mezani & Djamila Rekioua & Toufik Rekioua, 2023. "Different Control Techniques of Permanent Magnet Synchronous Motor with Fuzzy Logic for Electric Vehicles: Analysis, Modelling, and Comparison," Energies, MDPI, vol. 16(7), pages 1-28, March.
    3. Achikkulath Prasanthi & Hussain Shareef & Rachid Errouissi & Madathodika Asna & Addy Wahyudie, 2023. "Real-Time Implementation and Control of Multi-Source Electric Vehicle Traction Motor under Various Drive Conditions," Energies, MDPI, vol. 16(18), pages 1-31, September.

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