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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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    1. 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.
    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. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    4. Marco Cuturi & Mathieu Blondel, 2017. "Soft-DTW: a Differentiable Loss Function for Time-Series," Working Papers 2017-81, Center for Research in Economics and Statistics.
    5. 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.
    6. Diju Gao & Xuyang Wang & Tianzhen Wang & Yide Wang & Xiaobin Xu, 2018. "An Energy Optimization Strategy for Hybrid Power Ships under Load Uncertainty Based on Load Power Prediction and Improved NSGA-II Algorithm," Energies, MDPI, vol. 11(7), pages 1-14, July.
    7. Ovrum, E. & Bergh, T.F., 2015. "Modelling lithium-ion battery hybrid ship crane operation," Applied Energy, Elsevier, vol. 152(C), pages 162-172.
    8. 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.
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    3. 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).
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    5. 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).
    6. 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.
    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. 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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