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Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems

Author

Listed:
  • Wenshuo Tang

    (The School of Engineering and Physical Sciences, Smart System Group, Heriot Watt University, Edinburgh EH14 4AS, UK)

  • Darius Roman

    (The School of Engineering and Physical Sciences, Smart System Group, Heriot Watt University, Edinburgh EH14 4AS, UK)

  • Ross Dickie

    (The School of Engineering and Physical Sciences, Smart System Group, Heriot Watt University, Edinburgh EH14 4AS, UK)

  • Valentin Robu

    (The School of Engineering and Physical Sciences, Smart System Group, Heriot Watt University, Edinburgh EH14 4AS, UK)

  • David Flynn

    (The School of Engineering and Physical Sciences, Smart System Group, Heriot Watt University, Edinburgh EH14 4AS, UK)

Abstract

Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion.

Suggested Citation

  • Wenshuo Tang & Darius Roman & Ross Dickie & Valentin Robu & David Flynn, 2020. "Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems," Energies, MDPI, vol. 13(18), pages 1-29, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4676-:d:410691
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    References listed on IDEAS

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    Cited by:

    1. Aaron Shmaryahu & Nissim Amar & Alexander Ivanov & Ilan Aharon, 2021. "Sizing Procedure for System Hybridization Based on Experimental Source Modeling for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-21, August.
    2. Saurabh Saxena & Darius Roman & Valentin Robu & David Flynn & Michael Pecht, 2021. "Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning," Energies, MDPI, vol. 14(3), pages 1-17, January.
    3. Tsoumpris, Charalampos & Theotokatos, Gerasimos, 2023. "A decision-making approach for the health-aware energy management of ship hybrid power plants," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Bruce Stephen, 2022. "Machine Learning Applications in Power System Condition Monitoring," Energies, MDPI, vol. 15(5), pages 1-2, March.

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