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@INPROCEEDINGS{Abdollahi:1038430,
      author       = {Abdollahi, Farideh and Malek, Kourosh and Kadyk, Thomas and
                      Eikerling, Michael},
      title        = {{A}utonomous {D}ata {A}nalytics for {E}nhanced
                      {P}erformance and {L}ifetime {P}rediction in {PEM} {F}uel
                      {C}ells and {W}ater {E}lectrolyzers},
      reportid     = {FZJ-2025-01426},
      year         = {2024},
      abstract     = {Longevity is a crucial aspect in evaluating the economic
                      viability of polymer electrolyte fuel cells (PEFCs) in a
                      sustainable energy economy. Making reliable predictions on
                      the performance and lifetime of PEFCs remains challenging
                      due to the complex interplay of processes involved in their
                      operation, including those that drive degradation. The
                      prospects of forecasting PEFC performance with physical
                      models hinges on their completeness in terms of processes
                      accounted for and data available for parameterization.
                      Data-driven models, on the other hand, typically lack the
                      mechanical insight necessary for a deep understanding of
                      degradation causes. We, therefore, pursue the development of
                      a hybrid modeling approach that combines the capabilities of
                      physical models with the agility of data-driven techniques.
                      The aim of this approach is to evaluate the effectiveness of
                      physical models in forecasting performance and to assess
                      their ability for making reliable predictions about
                      performance degradation and lifetime. The combined approach
                      is anticipated to surpass separate physical and data-based
                      models in terms of accuracy, robustness, and
                      interpretability, providing a reliable foundation for
                      identifying maintenance needs and extending the lifespan of
                      PEFCs.},
      month         = {Jun},
      date          = {2024-06-12},
      organization  = {Helmholtz AI Conference 2024,
                       Dusseldorf (Germany), 12 Jun 2024 - 14
                       Jun 2024},
      subtyp        = {After Call},
      cin          = {IET-3},
      cid          = {I:(DE-Juel1)IET-3-20190226},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123)},
      pid          = {G:(DE-HGF)POF4-1231},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1038430},
}