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Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles

Author

Listed:
  • Ziad Al-Saadi

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

  • Duong Phan Van

    (Division of Mechatronics, Mechanical Engineering Institute, Vietnam Maritime University, Haiphong 180000, Vietnam)

  • Ali Moradi Amani

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

  • Mojgan Fayyazi

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

  • Samaneh Sadat Sajjadi

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

  • Dinh Ba Pham

    (Division of Mechatronics, Mechanical Engineering Institute, Vietnam Maritime University, Haiphong 180000, Vietnam)

  • Reza Jazar

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

  • Hamid Khayyam

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

Abstract

Automotive companies continue to develop integrated safety, sustainability, and reliability features that can help mitigate some of the most common driving risks associated with autonomous vehicles (AVs). Hybrid electric vehicles (HEVs) offer practical solutions to use control strategies to cut down fuel usage and emissions. AVs and HEVs are combined to take the advantages of each kind to solve the problem of wasting energy. This paper presents an intelligent driver assistance system, including adaptive cruise control (ACC) and an energy management system (EMS), for HEVs. Our proposed ACC determines the desired acceleration and safe distance with the lead car through a switched model predictive control (MPC) and a neuro-fuzzy (NF) system. The performance criteria of the switched MPC toggles between speed and distance control appropriately and its stability is mathematically proven. The EMS intelligently control the energy consumption based on ACC commands. The results show that the driving risk is extremely reduced by using ACC-MPC and ACC-NF, and the vehicle energy consumption by driver assistance system based on ACC-NF is improved by 2.6%.

Suggested Citation

  • Ziad Al-Saadi & Duong Phan Van & Ali Moradi Amani & Mojgan Fayyazi & Samaneh Sadat Sajjadi & Dinh Ba Pham & Reza Jazar & Hamid Khayyam, 2022. "Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9378-:d:877073
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    References listed on IDEAS

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    1. Duong Phan & Alireza Bab-Hadiashar & Reza Hoseinnezhad & Reza N. Jazar & Abhijit Date & Ali Jamali & Dinh Ba Pham & Hamid Khayyam, 2020. "Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles," Energies, MDPI, vol. 13(7), pages 1-16, April.
    2. Phan, Duong & Bab-Hadiashar, Alireza & Lai, Chow Yin & Crawford, Bryn & Hoseinnezhad, Reza & Jazar, Reza N. & Khayyam, Hamid, 2020. "Intelligent energy management system for conventional autonomous vehicles," Energy, Elsevier, vol. 191(C).
    3. Duong Phan & Ali Moradi Amani & Mirhamed Mola & Ahmad Asgharian Rezaei & Mojgan Fayyazi & Mahdi Jalili & Dinh Ba Pham & Reza Langari & Hamid Khayyam, 2021. "Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
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    1. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.

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