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Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context

Author

Listed:
  • Massinissa Graba

    (Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada)

  • Sousso Kelouwani

    (Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada)

  • Lotfi Zeghmi

    (Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada)

  • Ali Amamou

    (Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada)

  • Kodjo Agbossou

    (Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada)

  • Mohammad Mohammadpour

    (Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada)

Abstract

Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value.

Suggested Citation

  • Massinissa Graba & Sousso Kelouwani & Lotfi Zeghmi & Ali Amamou & Kodjo Agbossou & Mohammad Mohammadpour, 2020. "Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context," Sustainability, MDPI, vol. 12(20), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8541-:d:428713
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    References listed on IDEAS

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    1. Amin Ghobadpour & Ali Amamou & Sousso Kelouwani & Nadjet Zioui & Lotfi Zeghmi, 2020. "Impact of Powertrain Components Size and Degradation Level on the Energy Management of a Hybrid Industrial Self-Guided Vehicle," Energies, MDPI, vol. 13(19), pages 1-20, September.
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    Cited by:

    1. Mohammad Mohammadpour & Lotfi Zeghmi & Sousso Kelouwani & Marc-André Gaudreau & Ali Amamou & Massinissa Graba, 2021. "An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots," Energies, MDPI, vol. 14(12), pages 1-19, June.

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