h1

% 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”.

@PHDTHESIS{nlbayir:1021849,
      author       = {Ünlübayir, Cem},
      othercontributors = {Sauer, Dirk Uwe and van Biert, Lindert},
      title        = {{I}ntelligent operating methods and their influence on
                      components for hybrid marine propulsion systems},
      volume       = {197},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {Institute for Power Electronics and Electrical Drives
                      (ISEA), RWTH Aachen University},
      reportid     = {RWTH-2025-09689},
      series       = {Aachener Beiträge des ISEA},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2025},
      abstract     = {This dissertation explores sustainable shipping by
                      developing an electric propulsion system using
                      high-temperature fuel cells and batteries. As shipping is a
                      major emitter of greenhouse gases, alternative propulsion
                      systems are essential for the global transition towards
                      emission-free mobility. Batteries and fuel cells offer high
                      efficiency, energy density, and technological feasibility
                      for marine applications, enabling long-range cruising and
                      dynamic maneuvering. This work focuses on the
                      simulation-based analysis and the hardware validation of
                      operating methods for a hybrid marine propulsion system.
                      High-temperature fuel cells present unique technical
                      challenges that require scientific investigation. The thesis
                      develops energy management methods for efficient power
                      distribution between propulsion components, tested and
                      validated on a hardware-in-the-loop test bench using a 40
                      kWh battery system and a 32 kW fuel cell system. The work
                      closes a research gap in maritime research, addressing
                      large-scale electrified propulsion for cruise ships. Current
                      solutions primarily retrofit existing systems or explore
                      alternative fuels. However, previous studies focused on
                      short-range ships like ferries, unsuitable for cruise
                      operations. Advanced operating methods significantly improve
                      drivetrain resource efficiency, reducing fuel consumption
                      and extending component lifespan while lowering operating
                      costs. Machine learning techniques, including reinforcement
                      learning, enhance predictive and optimized energy
                      management. The study validates its approach through
                      hardware tests, confirming the propulsion system’s
                      feasibility as an alternative to conventional marine
                      engines.},
      cin          = {618310 / 614500},
      ddc          = {621.3},
      cid          = {$I:(DE-82)618310_20140620$ / $I:(DE-82)614500_20201203$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2025-09689},
      url          = {https://publications.rwth-aachen.de/record/1021849},
}