Optimisation of Hybrid Energy Systems for Maritime Vessels

David Flynn, Wenshuo Tang, Darius Roman, Ross Dickie, Valentin Robu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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The decarbonisastion agenda in maritime transport requires that asset owners and operators adopt greener technologies within their existing and new vessels. The primary drivers within this agenda relate to improved environmental metrics, efficient energy performance, and improved asset management, however, the integration of new technologies always presents technical and financial risks. In this paper, utilising energy and environmental monitoring from real vessels, we propose an energy system optimisation architecture, Hybrid Fusion Energy Management System (HyFES), that optimises the key performance indicators of energy performance, reduction of diesel engine NOx (Nitrogen Oxide) and PM (Particulate Matter), and prognostic state of health assessment of energy storage technologies. Using state of the art machine learning techniques, we are able to determine the on-board lithium-ion and lead acid batteries’ state of health with accuracy greater than 3% and 8%, respectively. Dependent on the mode of operation, optimisation of energy performance indicates fuel saving of between 70-80% for the vessel operator. Future research will focus on the integration of more assets into the optimisation architecture and increased vessel journey use cases.
Original languageEnglish
Title of host publication9th International Conference on Power Electronics, Machines and Drives
PublisherInstitution of Engineering and Technology
Number of pages6
ISBN (Print)9781785618215
Publication statusPublished - 18 Apr 2018
Event9th International Conference on Power Electronics, Machines and Drives 2018 - Liverpool ACC, Liverpool, United Kingdom
Duration: 17 Apr 201819 Apr 2018


Conference9th International Conference on Power Electronics, Machines and Drives 2018
Abbreviated titlePEMD 2018
Country/TerritoryUnited Kingdom
Internet address


  • energy systems
  • health management
  • embedded intelligence
  • Battery
  • Prognostics


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