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      Optimisation of hybrid energy systems for maritime vessels

      research-article

      , , , ,

      The Journal of Engineering

      The Institution of Engineering and Technology

      The 9th International Conference on Power Electronics, Machines and Drives (PEMD 2018) (PEMD 2018)

      17–19 April 2018

      exhaust systems, diesel engines, hybrid electric vehicles, battery management systems, asset management, energy conservation, energy management systems, nitrogen compounds, energy storage, transportation, lead acid batteries, air pollution control, condition monitoring, learning (artificial intelligence), ships, power engineering computing, hybrid energy systems, maritime vessels, decarbonisation agenda, maritime transport, asset owners, greener technologies, primary drivers, efficient energy performance, asset management, technical risks, financial risks, utilising energy, environmental monitoring, energy system optimisation architecture, hybrid fusion energy management system, key performance indicators, diesel engine nitrogen oxide, energy storage technologies, art machine-learning techniques, lead acid batteries, vessel operator, increased vessel journey use cases, environmental metrics, HyFES, energy performance, particulate matter, prognostic state of health assessment, on-board lithium-ion batteries

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          Abstract

          The decarbonisation 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. Here, utilising energy and environmental monitoring from real vessels, the authors 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 nitrogen oxide (NOx), and particulate matter (PM), and prognostic state of health assessment of energy storage technologies. Using state of the art machine-learning techniques, the authors are able to determine the on-board lithium-ion and lead acid batteries' state of health with accuracy > 8 and 4%, respectively. Dependent on the mode of operation, optimisation of energy performance indicates fuel saving of between 70 and 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.

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          Most cited references 4

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          Sparse Bayesian learning and the relevance vector machine

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            Model-based prognostics of gear health using stochastic dynamical models

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              Past, Present, and Future Challenges of the Marine Vessel’s Electrical Power System

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                Author and article information

                Contributors
                Conference
                JOE
                The Journal of Engineering
                J. Eng.
                The Institution of Engineering and Technology
                2051-3305
                22 January 2019
                26 April 2019
                June 2019
                : 2019
                : 17
                : 4516-4521
                Affiliations
                School of Engineering and Physical Science, Smart Systems Group, Heriot-Watt University , UK
                Article
                JOE.2018.8232 JOE.2018.8232
                10.1049/joe.2018.8232

                This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

                The 9th International Conference on Power Electronics, Machines and Drives (PEMD 2018)
                PEMD 2018
                9
                Liverpool, UK
                17–19 April 2018
                Page count
                Pages: 0
                Product
                Categories
                ee-cer
                cn-pmd-2018
                The 9th International Conference on Power Electronics, Machines and Drives (PEMD 2018)

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