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

Mohammad Ghazali, Ishaan Gupta, Kemal Buyukkabasakal, Mohamed Amine Ben Abdallah, Caner Harman, Berfin Kahraman and Ahu Ece Hartavi ()
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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

Energies, 2025, vol. 18, issue 11, 1-18

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.

Keywords: semi-autonomous truck; multiple model controller; particle swarm optimization; longitudinal velocity tracking controller; adaptive control strategy; nature-inspired AI (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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