Energy Management of a Semi-Autonomous Truck Using a Blended Multiple Model Controller Based on Particle Swarm Optimization
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 collecte...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/11/2893 |
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| Summary: | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.19</mn><mo>%</mo></mrow></semantics></math></inline-formula> in autonomous mode and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.534</mn><mo>%</mo></mrow></semantics></math></inline-formula> in manual mode compared to traditional fixed-gain approaches. |
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| ISSN: | 1996-1073 |