IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i11p2893-d1669005.html
   My bibliography  Save this article

Energy Management of a Semi-Autonomous Truck Using a Blended Multiple Model Controller Based on Particle Swarm Optimization

Author

Listed:
  • Mohammad Ghazali

    (Software-Defined Electric and Autonomous Vehicles, Fleets, and Infrastructure Laboratory, School of Engineering, Faculty of Engineering & Physical Sciences, University of Surrey, Guildford GU2 7XH, UK)

  • Ishaan Gupta

    (Software-Defined Electric and Autonomous Vehicles, Fleets, and Infrastructure Laboratory, School of Engineering, Faculty of Engineering & Physical Sciences, University of Surrey, Guildford GU2 7XH, UK)

  • Kemal Buyukkabasakal

    (Software-Defined Electric and Autonomous Vehicles, Fleets, and Infrastructure Laboratory, School of Engineering, Faculty of Engineering & Physical Sciences, University of Surrey, Guildford GU2 7XH, UK)

  • Mohamed Amine Ben Abdallah

    (Software-Defined Electric and Autonomous Vehicles, Fleets, and Infrastructure Laboratory, School of Engineering, Faculty of Engineering & Physical Sciences, University of Surrey, Guildford GU2 7XH, UK)

  • Caner Harman

    (Ford Motor Company, Dearborn, MI 48124, USA)

  • Berfin Kahraman

    (Ford-Otosan, 34885 Istanbul, Türkiye)

  • Ahu Ece Hartavi

    (Software-Defined Electric and Autonomous Vehicles, Fleets, and Infrastructure Laboratory, School of Engineering, Faculty of Engineering & Physical Sciences, University of Surrey, Guildford GU2 7XH, UK)

Abstract

Recently, the electrification and automation of heavy-duty trucks has gained significant attention from both industry and academia, driven by new legislation introduced by the European Union. During a typical drive cycle, the mass of an urban service truck can vary substantially as waste is collected, yet most existing studies rely on a single controller with fixed gains. This limits the ability to adapt to mass changes and results in suboptimal energy usage. Within the framework of the EU-funded OBELICS and ESCALATE projects, this study proposes a novel control strategy for a semi-autonomous refuse truck. The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. The main objectives of the proposed method are to (i) optimize controller parameters, (ii) reduce overall energy consumption, and (iii) minimize speed tracking error. A cost function addressing these objectives is formulated for both autonomous and manual driving modes. The strategy is evaluated using a real-world drive cycle from Eskişehir City, Turkiye. Simulation results show that the proposed MMC-based method improves vehicle performance by 5.19 % in autonomous mode and 0.534 % in manual mode compared to traditional fixed-gain approaches.

Suggested Citation

  • Mohammad Ghazali & Ishaan Gupta & Kemal Buyukkabasakal & Mohamed Amine Ben Abdallah & Caner Harman & Berfin Kahraman & Ahu Ece Hartavi, 2025. "Energy Management of a Semi-Autonomous Truck Using a Blended Multiple Model Controller Based on Particle Swarm Optimization," Energies, MDPI, vol. 18(11), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2893-:d:1669005
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/11/2893/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/11/2893/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aimin Du & Yaoyi Chen & Dongxu Zhang & Yeyang Han, 2021. "Multi-Objective Energy Management Strategy Based on PSO Optimization for Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 14(9), pages 1-18, April.
    2. Jiajia Liang & Xiangyang Xu & Peng Dong & Tao Feng & Wei Guo & Shuhan Wang, 2022. "Energy Management Strategy of a Novel Electric Dual-Motor Transmission for Heavy Commercial Vehicles Based on APSO Algorithm," Sustainability, MDPI, vol. 14(3), pages 1-12, January.
    Full references (including those not matched with items on IDEAS)

    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. Tian, Yang & Zhang, Yahui & Li, Hongmin & Gao, Jinwu & Swen, Austin & Wen, Guilin, 2023. "Optimal sizing and energy management of a novel dual-motor powertrain for electric vehicles," Energy, Elsevier, vol. 275(C).
    2. Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    3. Xiaohui Liu & Yiwei Wu & Jingyun Zhang & Yifan Zhao & Yangming Hu & Xianghai Yan, 2025. "Research on energy optimization control strategy for parallel hybrid tractor based on AIPSO," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-28, February.
    4. Gianluca Brando & Adolfo Dannier & Andrea Del Pizzo, 2022. "Efficiency Analytical Characterization for Brushless Electric Drives," Energies, MDPI, vol. 15(8), pages 1-11, April.

    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:jeners:v:18:y:2025:i:11:p:2893-:d:1669005. 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.