IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v251y2019ic13.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919309705
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113308?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Hou, Jun & Song, Ziyou & Park, Hyeongjun & Hofmann, Heath & Sun, Jing, 2018. "Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems," Applied Energy, Elsevier, vol. 230(C), pages 62-77.
    3. Geertsma, R.D. & Negenborn, R.R. & Visser, K. & Hopman, J.J., 2017. "Design and control of hybrid power and propulsion systems for smart ships: A review of developments," Applied Energy, Elsevier, vol. 194(C), pages 30-54.
    4. Zheng, Huarong & Negenborn, Rudy R. & Lodewijks, Gabriël, 2017. "Closed-loop scheduling and control of waterborne AGVs for energy-efficient Inter Terminal Transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 261-278.
    5. Hou, Jun & Sun, Jing & Hofmann, Heath, 2018. "Adaptive model predictive control with propulsion load estimation and prediction for all-electric ship energy management," Energy, Elsevier, vol. 150(C), pages 877-889.
    6. Geertsma, R.D. & Negenborn, R.R. & Visser, K. & Loonstijn, M.A. & Hopman, J.J., 2017. "Pitch control for ships with diesel mechanical and hybrid propulsion: Modelling, validation and performance quantification," Applied Energy, Elsevier, vol. 206(C), pages 1609-1631.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Latif, Abdul & Hussain, S. M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2021. "Double stage controller optimization for load frequency stabilization in hybrid wind-ocean wave energy based maritime microgrid system," Applied Energy, Elsevier, vol. 282(PA).
    2. 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.
    3. 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).
    4. Daraz, Amil, 2023. "Optimized cascaded controller for frequency stabilization of marine microgrid system," Applied Energy, Elsevier, vol. 350(C).
    5. Dong, Zhe & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2020. "Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system," Applied Energy, Elsevier, vol. 259(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.

    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. Hou, Jun & Song, Ziyou & Park, Hyeongjun & Hofmann, Heath & Sun, Jing, 2018. "Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems," Applied Energy, Elsevier, vol. 230(C), pages 62-77.
    2. Yuan, Yupeng & Wang, Jixiang & Yan, Xinping & Shen, Boyang & Long, Teng, 2020. "A review of multi-energy hybrid power system for ships," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    3. 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).
    4. 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.
    5. Miretti, Federico & Misul, Daniela & Gennaro, Giulio & Ferrari, Antonio, 2022. "Hybridizing waterborne transport: Modeling and simulation of low-emissions hybrid waterbuses for the city of Venice," Energy, Elsevier, vol. 244(PB).
    6. 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).
    7. 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).
    8. Wang, Zhuang & Chen, Li & Wang, Bin & Huang, Lianzhong & Wang, Kai & Ma, Ranqi, 2023. "Integrated optimization of speed schedule and energy management for a hybrid electric cruise ship considering environmental factors," Energy, Elsevier, vol. 282(C).
    9. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Liu, Zhijiang & Ke, Yun & Ding, Shunliang, 2023. "Novel enhancement of energy distribution for marine hybrid propulsion systems by an advanced variable weight decision model predictive control," Energy, Elsevier, vol. 274(C).
    10. Bouguenna, Ibrahim Farouk & Azaiz, Ahmed & Tahour, Ahmed & Larbaoui, Ahmed, 2019. "Robust neuro-fuzzy sliding mode control with extended state observer for an electric drive system," Energy, Elsevier, vol. 169(C), pages 1054-1063.
    11. Mohsen Banaei & Fatemeh Ghanami & Mehdi Rafiei & Jalil Boudjadar & Mohammad-Hassan Khooban, 2020. "Energy Management of Hybrid Diesel/Battery Ships in Multidisciplinary Emission Policy Areas," Energies, MDPI, vol. 13(16), pages 1-16, August.
    12. Hou, Jun & Song, Ziyou, 2020. "A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity," Applied Energy, Elsevier, vol. 257(C).
    13. Monaaf D. A. Al-Falahi & Tomasz Tarasiuk & Shantha Gamini Jayasinghe & Zheming Jin & Hossein Enshaei & Josep M. Guerrero, 2018. "AC Ship Microgrids: Control and Power Management Optimization," Energies, MDPI, vol. 11(6), pages 1-20, June.
    14. Nuchturee, Chalermkiat & Li, Tie & Xia, Hongpu, 2020. "Energy efficiency of integrated electric propulsion for ships – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    15. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Yang, Qidong & Yang, Xuchang, 2022. "Optimal control of transient processes in marine hybrid propulsion systems: Modeling, optimization and performance enhancement," Applied Energy, Elsevier, vol. 321(C).
    16. Fan, Ailong & Wang, Junteng & He, Yapeng & Perčić, Maja & Vladimir, Nikola & Yang, Liu, 2021. "Decarbonising inland ship power system: Alternative solution and assessment method," Energy, Elsevier, vol. 226(C).
    17. Tang, Ruoli & Li, Xin & Lai, Jingang, 2018. "A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization," Applied Energy, Elsevier, vol. 228(C), pages 254-264.
    18. Daraz, Amil, 2023. "Optimized cascaded controller for frequency stabilization of marine microgrid system," Applied Energy, Elsevier, vol. 350(C).
    19. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    20. Nicu Bizon & Alin Gheorghita Mazare & Laurentiu Mihai Ionescu & Phatiphat Thounthong & Erol Kurt & Mihai Oproescu & Gheorghe Serban & Ioan Lita, 2019. "Better Fuel Economy by Optimizing Airflow of the Fuel Cell Hybrid Power Systems Using Fuel Flow-Based Load-Following Control," Energies, MDPI, vol. 12(14), pages 1-17, July.

    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:eee:appene:v:251:y:2019:i:c:13. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.