IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i18p10113-d632260.html
   My bibliography  Save this article

Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation

Author

Listed:
  • Duong Phan

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

  • Ali Moradi Amani

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

  • Mirhamed Mola

    (School of Electrical and Computer Engineering, Shiraz University, Shiraz 71348, Iran)

  • Ahmad Asgharian Rezaei

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

  • Mojgan Fayyazi

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

  • Mahdi Jalili

    (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 Langari

    (Engineering Technology and Industrial Distribution (ETID), Texas A&M University (TAMU), College Station, TX 77843, USA)

  • Hamid Khayyam

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

Abstract

A sustainable circular economy involves designing and promoting new products with the least environmental impact through increasing efficiency. The emergence of autonomous vehicles (AVs) has been a revolution in the automobile industry and a breakthrough opportunity to create more sustainable transportation in the future. Autonomous vehicles are supposed to provide a safe, easy-to-use and environmentally friendly means of transport. To this end, improving AVs’ safety and energy efficiency by using advanced control and optimization algorithms has become an active research topic to deliver on new commitments: carbon reduction and responsible innovation. The focus of this study is to improve the energy consumption of an AV in a vehicle-following process while safe driving is satisfied. We propose a cascade control system in which an autonomous cruise controller (ACC) is integrated with an energy management system (EMS) to reduce energy consumption. An adaptive model predictive control (AMPC) is proposed as the ACC to control the acceleration of the ego vehicle (the following vehicle) in a vehicle-following scenario, such that it can safely follow the lead vehicle in the same lane on a highway. The proposed ACC appropriately switches between speed and distance control systems to follow the lead vehicle safely and precisely. The computed acceleration is then used in the EMS component to find the optimal engine torque that minimizes the fuel consumption of the ego vehicle. EMS is designed based on two methods: type 1 fuzzy logic system (T1FLS) and interval type 2 fuzzy logic system (IT2FLS). Results show that the combination of AMPC and IT2FLS significantly reduces fuel consumption while the ego vehicle follows the lead vehicle safely and with a minimum spacing error. The proposed controller facilitates smarter energy use in AVs and supports safer transportation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10113-:d:632260
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/18/10113/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/18/10113/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Khayyam, Hamid & Naebe, Minoo & Milani, Abbas S. & Fakhrhoseini, Seyed Mousa & Date, Abhijit & Shabani, Bahman & Atkiss, Steve & Ramakrishna, Seeram & Fox, Bronwyn & Jazar, Reza N., 2021. "Improving energy efficiency of carbon fiber manufacturing through waste heat recovery: A circular economy approach with machine learning," Energy, Elsevier, vol. 225(C).
    2. 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.
    3. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Kawtar Rahaoui & Hamid Khayyam & Quoc Linh Ve & Aliakbar Akbarzadeh & Abhijit Date, 2021. "Renewable Thermal Energy Driven Desalination Process for a Sustainable Management of Reverse Osmosis Reject Water," Sustainability, MDPI, vol. 13(19), pages 1-15, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2021. "Sustainable advanced distribution management system design considering differential pricing schemes and carbon emissions," Energy, Elsevier, vol. 219(C).
    2. Cheng, Shen & Zhao, Gaiju & Gao, Ming & Shi, Yuetao & Huang, Mingming & Yousefi, Nasser, 2021. "Optimal hybrid energy system for locomotive utilizing improved Locust Swarm optimizer," Energy, Elsevier, vol. 218(C).
    3. 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.
    4. Li, Tianyu & Liu, Huiying & Wang, Hui & Yao, Yongming, 2020. "Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles," Energy, Elsevier, vol. 198(C).
    5. Kawtar Rahaoui & Hamid Khayyam & Quoc Linh Ve & Aliakbar Akbarzadeh & Abhijit Date, 2021. "Renewable Thermal Energy Driven Desalination Process for a Sustainable Management of Reverse Osmosis Reject Water," Sustainability, MDPI, vol. 13(19), pages 1-15, September.
    6. Lu, Zhiming & Gao, Yan & Xu, Chuanbo, 2021. "Evaluation of energy management system for regional integrated energy system under interval type-2 hesitant fuzzy environment," Energy, Elsevier, vol. 222(C).
    7. 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.
    8. Cezar-Petre Simion & Cătălin-Alexandru Verdeș & Alexandra-Andreea Mironescu & Florin-Gabriel Anghel, 2023. "Digitalization in Energy Production, Distribution, and Consumption: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-30, February.
    9. Zain Anwar Ali & Mahreen Zain & M. Salman Pathan & Peter Mooney, 2024. "Contributions of artificial intelligence for circular economy transition leading toward sustainability: an explorative study in agriculture and food industries of Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(8), pages 19131-19175, August.
    10. Zeinab Farshadfar & Tomasz Mucha & Kari Tanskanen, 2024. "Leveraging Machine Learning for Advancing Circular Supply Chains: A Systematic Literature Review," Logistics, MDPI, vol. 8(4), pages 1-25, October.
    11. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    12. Shi, Yao & Lin, Runze & Wu, Xialai & Zhang, Zhiming & Sun, Pei & Xie, Lei & Su, Hongye, 2022. "Dual-mode fast DMC algorithm for the control of ORC based waste heat recovery system," Energy, Elsevier, vol. 244(PA).
    13. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2023. "Industry 5.0 and Triple Bottom Line Approach in Supply Chain Management: The State-of-the-Art," Sustainability, MDPI, vol. 15(7), pages 1-30, March.
    14. Juraj Šebo & Miriam Šebová & Iztok Palčič, 2021. "Implementation of Circular Economy Technologies: An Empirical Study of Slovak and Slovenian Manufacturing Companies," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    15. Elahi, Ehsan & Khalid, Zainab, 2022. "Estimating smart energy inputs packages using hybrid optimisation technique to mitigate environmental emissions of commercial fish farms," Applied Energy, Elsevier, vol. 326(C).
    16. Despotovic, Miroslav & Glatschke, Matthias, 2024. "Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy," SocArXiv 6qmhf, Center for Open Science.
    17. Kyle Pender & Konstantinos Bacharoudis & Filippo Romoli & Peter Greaves & Jonathan Fuller, 2024. "Feasibility of Natural Fibre Usage for Wind Turbine Blade Components: A Structural and Environmental Assessment," Sustainability, MDPI, vol. 16(13), pages 1-31, June.
    18. Ilyes Tegani & Okba Kraa & Haitham S. Ramadan & Mohamed Yacine Ayad, 2023. "Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory," Energies, MDPI, vol. 16(13), pages 1-23, June.
    19. Daniel Silva & Ricardo Rocha & Filipe Ribeiro & Helena Monteiro, 2024. "Environmental Impact of an Innovative Aeronautic Carbon Composite Manufactured via Heated Vacuum-Assisted Resin Transfer Molding," Sustainability, MDPI, vol. 16(8), pages 1-17, April.
    20. Kristia Kristia & Mohammad Fazle Rabbi, 2023. "Exploring the Synergy of Renewable Energy in the Circular Economy Framework: A Bibliometric Study," Sustainability, MDPI, vol. 15(17), pages 1-27, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10113-:d:632260. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.