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Ship energy management system development and experimental evaluation utilizing marine loading cycles based on machine learning techniques

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  • Planakis, Nikolaos
  • Papalambrou, George
  • Kyrtatos, Nikolaos

Abstract

In order to develop energy management systems for hybrid ship propulsion plants that are truly optimal and robust, it is important that the test conditions in experimental facilities are as close as possible to real world applications. In this context, a framework for the design and experimental evaluation of power-split control systems for ship propulsion is proposed. Using machine learning, data from ship operation are processed and 20 loading patterns are recognized; representative templates are extracted to be used as marine loading cycles in the energy management system development and testing. A ship propulsion model with wave disturbance is utilized to simulate realistic loading scenarios on the experimental facility. A predictive energy management system is presented, that controls the diesel engine and the electric motor/generator based on a strategy that defines the trade-off between fuel consumption and NOx emissions minimization. In addition the propeller load characteristics that are estimated and a speed predictor are utilized to aid the optimization within the 10 s prediction time window. A parametric simulation study is performed for the trade-off evaluation between fuel consumption and NOx emissions reduction potential of the control scheme. Finally, utilizing an extracted loading cycle, the energy management system is experimentally implemented and tested in real-time operation, where it has to cope with environmental disturbance rejection and follow the desired speed profile while performing the power-split control in respect to the fuel to NOx weighting strategy. Based on the experimental results in a hybrid diesel–electric marine powertrain with a 260 kW diesel engine and a 90 kW electric machine, fuel consumption and NOx emissions reduction by 6% and 8.5% respectively, were achieved over the tested profile. In this framework, the capabilities of the energy management system in realistic operation conditions can be exploited and evaluated.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s0306261921013702
    DOI: 10.1016/j.apenergy.2021.118085
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    References listed on IDEAS

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    2. Giovanni Lucà Trombetta & Salvatore Gianluca Leonardi & Davide Aloisio & Laura Andaloro & Francesco Sergi, 2024. "Lithium-Ion Batteries on Board: A Review on Their Integration for Enabling the Energy Transition in Shipping Industry," Energies, MDPI, vol. 17(5), pages 1-37, February.
    3. 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).
    4. Xiaojun Sun & Yingbo Gao & Qiao Zhang & Shunliang Ding, 2024. "Machine Learning-Based Extraction Method for Marine Load Cycles with Environmentally Sustainable Applications," Sustainability, MDPI, vol. 16(11), pages 1-21, June.
    5. 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).
    6. Tayfun Uyanık & Nur Najihah Abu Bakar & Özcan Kalenderli & Yasin Arslanoğlu & Josep M. Guerrero & Abderezak Lashab, 2023. "A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study," Energies, MDPI, vol. 16(13), pages 1-20, June.
    7. 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).
    8. Yang, Ying & Liu, Yang & Li, Guorong & Zhang, Zekun & Liu, Yanbin, 2024. "Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).

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