% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{nlbayir:1024569,
      author       = {Ünlübayir, Cem and Mierendorff, Ulrich Hermann and
                      Börner, Martin Florian and Quade, Katharina Lilith and
                      Blömeke, Alexander and Ringbeck, Florian and Sauer, Dirk
                      Uwe},
      title        = {{A} {D}ata-{D}riven {A}pproach to {S}hip {E}nergy
                      {M}anagement: {I}ncorporating {A}utomated {T}racking
                      {S}ystem {D}ata and {W}eather {I}nformation},
      journal      = {Journal of marine science and engineering},
      volume       = {11},
      number       = {12},
      issn         = {2077-1312},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2024-02245},
      pages        = {2259 -},
      year         = {2023},
      abstract     = {This research paper presents a data-based energy management
                      method for a vessel that predicts the upcoming load demands
                      based on data from weather information and its automated
                      tracking system. The vessel is powered by a hybrid
                      propulsion system consisting of a high-temperature fuel cell
                      system to cover the base load and a battery system to
                      compensate for the fuel cell’s limited dynamic response
                      capability to load fluctuations. The developed energy
                      management method predicts the load demand of the next time
                      steps by analyzing physical relationships utilizing
                      operational and positional data of a real vessel. This
                      allows a steadier operation of the fuel cell and reduces
                      stress factors leading to accelerated aging and increasing
                      the resource efficiency of the propulsion system. Since
                      large ships record tracking data of their cruise and no a
                      priori training is required to adjust the energy management,
                      the proposed method can be implemented with small additional
                      computational effort. The functionality of the energy
                      management method was verified using data from a real ship
                      and records of the water currents in the North Sea. The
                      accuracy of the load prediction is $2.7\%$ and the
                      attenuation of the fuel cell’s power output could be
                      increased by approximately $32\%.$},
      cin          = {IEK-12},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IEK-12-20141217},
      pnm          = {1223 - Batteries in Application (POF4-122)},
      pid          = {G:(DE-HGF)POF4-1223},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001131264900001},
      doi          = {10.3390/jmse11122259},
      url          = {https://juser.fz-juelich.de/record/1024569},
}