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Model predictive maneuvering control and energy management for all-electric autonomous ships

Author

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  • Haseltalab, Ali
  • Negenborn, Rudy R.

Abstract

Over the last few years, autonomous shipping has been under extensive investigation by the scientific community where the main focus has been on ship maneuvering control and not on the optimal use of energy sources. In this paper, the purpose is to bridge the gap between maneuvering control, energy management, and the control of the Power and Propulsion System (PPS) to improve fuel efficiency and the performance of the vessel. Maneuvering control, energy management, and the control of the PPS are in the literature typically studied independently from one another, while they are closely connected. A generic control methodology based on receding horizon control techniques is proposed for the ship maneuvering control as well as energy management. In the context of this research, Direct Current (DC) all-electric architectures are considered for the PPS where the relationship between the produced power by energy sources and vessel propellers is established by a DC microgrid. The objective of the proposed approach is to ensure the ship mission objectives by guaranteeing efficient power availability, decreasing the trajectory tracking error, and increasing the fuel efficiency. In this regard, for the ship motion control, a Model Predictive Control (MPC) algorithm is proposed which is based on Input–Output Feedback Linearization (IOFL). Through this algorithm, the required power for the ship mission is predicted and then, transferred to the proposed Predictive Energy Management (PEM) algorithm which decides on the optimal split between different on-board energy sources during the mission. As a result, the fuel efficiency and the power system stability can be increased. Several simulations are carried out for the evaluation of the proposed approach. The results suggest that by adopting the proposed approach, the trajectory tracking error decreases and the Specific Fuel Consumption (SFC) efficiency is significantly improved.

Suggested Citation

  • Haseltalab, Ali & Negenborn, Rudy R., 2019. "Model predictive maneuvering control and energy management for all-electric autonomous ships," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:13
    DOI: 10.1016/j.apenergy.2019.113308
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    References listed on IDEAS

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    1. Geertsma, R.D. & Visser, K. & Negenborn, R.R., 2018. "Adaptive pitch control for ships with diesel mechanical and hybrid propulsion," Applied Energy, Elsevier, vol. 228(C), pages 2490-2509.
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    Cited by:

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    3. Fonseca, Tiago & Lagdami, Khanssa & Schröder-Hinrichs, Jens-Uwe, 2021. "Assessing innovation in transport: An application of the Technology Adoption (TechAdo) model to Maritime Autonomous Surface Ships (MASS)," Transport Policy, Elsevier, vol. 114(C), pages 182-195.
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    5. Planakis, Nikolaos & Papalambrou, George & Kyrtatos, Nikolaos, 2022. "Ship energy management system development and experimental evaluation utilizing marine loading cycles based on machine learning techniques," Applied Energy, Elsevier, vol. 307(C).
    6. Xie, Peilin & Tan, Sen & Bazmohammadi, Najmeh & Guerrero, Josep. M. & Vasquez, Juan. C. & Alcala, Jose Matas & Carreño, Jorge El Mariachet, 2022. "A distributed real-time power management scheme for shipboard zonal multi-microgrid system," Applied Energy, Elsevier, vol. 317(C).
    7. Park, Chybyung & Jeong, Byongug & Zhou, Peilin, 2022. "Lifecycle energy solution of the electric propulsion ship with Live-Life cycle assessment for clean maritime economy," Applied Energy, Elsevier, vol. 328(C).
    8. Maja Perčić & Nikola Vladimir & Marija Koričan, 2021. "Electrification of Inland Waterway Ships Considering Power System Lifetime Emissions and Costs," Energies, MDPI, vol. 14(21), pages 1-25, October.

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