A health-aware energy management strategy for autonomous ships power plants operation

Charalampos Tsoumpris, Gerasimos Theotokatos

Research output: Contribution to journalConference Contributionpeer-review

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Abstract

Autonomous shipping developments have gathered increasing interest in the maritime industry. In this respect, innovative solutions are required to operate autonomous ships without the direct intervention of a human operator. This study focuses on the development of a health-aware energy management strategy for the operation of autonomous ship power plants. To enable autonomous decisions, it is essential to acquire sufficient situational awareness based on the health state of the machinery, using diagnosis and prognosis tools. In this respect, a dynamic Bayesian network (DBN) approach is adopted to calculate components and system reliability. The predictive information along with the operating profile are considered in an enhanced energy management strategy, to prolong the power plant’s lifetime and avoid hazardous or degraded states whilst optimizing fuel consumption. To demonstrate the applicability of the proposed approach, a parallel hybrid power plant is selected as a case study. The investigated plant energy management is based on the equivalent consumption minimization strategy (ECMS). The results demonstrate that the most critical component is the engine while the electrical components have lower failure rates. By using the proposed strategy, the degradation of the engine can be attenuated, and the plant operation in safer regions can be achieved.
Original languageEnglish
Number of pages8
JournalTransportation Research Procedia
Publication statusAccepted/In press - 17 Nov 2022
EventTransport Research Arena 2022 - Lisbon, Portugal
Duration: 13 Nov 202217 Nov 2022

Keywords

  • autonomous ship
  • hybrid propulsion
  • hybrid energy management strategy
  • prognostic decision-making
  • dynamic Bayesian network

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