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@ARTICLE{Raman:1051608,
      author       = {Raman, K. Ashoke and Wolf, Niklas L. and Javed, Ali and
                      Karyofylli, Violeta and Kungl, Hans and Karl, André and
                      Jodat, Eva and Eichel, Rüdiger-A.},
      title        = {{E}valuating activation strategies and their stability on
                      {PEM} water electrolyzers using machine learning},
      journal      = {Energy and AI},
      volume       = {22},
      issn         = {2666-5468},
      address      = {Amsterdam},
      publisher    = {Elsevier ScienceDirect},
      reportid     = {FZJ-2026-00531},
      pages        = {100623},
      year         = {2025},
      abstract     = {Pre-treatment of the proton exchange membrane water
                      electrolyzers is a crucial procedure performed prior to its
                      regular operation. These procedures help in catalyst
                      activation and membrane saturation, thereby, ensuring its
                      optimal performance. In this study, we use machine learning
                      to investigate the impact of three distinct activation
                      procedures on the cell performance and stability. The data
                      set necessary to develop the surrogate models was obtained
                      from a lab scale PEM electrolyzer cell. After evaluating the
                      performance of the three tested models and validating them
                      with experimental data, extreme gradient boosting is
                      selected as the to perform parametric analysis. The modeling
                      predictions reveal that the activation procedures mainly
                      impact the ohmic resistance at the beginning of the cell
                      life. These observations were further corroborated using
                      through sensitivity analysis performed through an
                      explainable artificial intelligence technique. Furthermore,
                      data-driven time-series forecasting analysis to predict cell
                      stability for different activation procedures showed a good
                      comparison between experimental data and model predictions},
      cin          = {IET-1},
      ddc          = {624},
      cid          = {I:(DE-Juel1)IET-1-20110218},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123)},
      pid          = {G:(DE-HGF)POF4-1231},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.egyai.2025.100623},
      url          = {https://juser.fz-juelich.de/record/1051608},
}