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@INPROCEEDINGS{Mnken:1019896,
      author       = {Mänken, Christian and Schäfer, Christian and Kunz, Felix},
      othercontributors = {Eichel, Rüdiger-A.},
      title        = {{S}olid {O}xide {C}ell {S}tate-of-{H}ealth {P}rediction
                      with {M}achine {L}earning},
      reportid     = {FZJ-2023-05717},
      year         = {2023},
      abstract     = {To better understand degradation in electrochemical
                      converters and helping to correlate certain phenomena with
                      specific operating conditions, machine learning (ML) methods
                      are increasingly being applied. Success has already been
                      achieved in the field of degradation analysis and prediction
                      of capacity of Lithium ion batteries [1], for instance. In
                      terms of Solix Oxide Cell (SOC) stacks ML methods have been
                      applied mainly with the aim of identification of faulty
                      operation modes and degradation related fault diagnosis [2].
                      ML approaches usually require a considerable amount of real
                      training data, when used for forecasting models. A data
                      consolidation and curation strategy was developed with the
                      aim of processing the historic long-term test bench data of
                      SOCs collected by Forschungszentrum Jülich over the past
                      years. In comparison to other datasets developed in this
                      field [3], the one presented in this work contains SOC stack
                      tests in fuel cell operation with significantly longer
                      operating times under load. A compilation of the sample
                      experiments and the consolidation into a hierarchical data
                      format are presented. Further, an essential part of the
                      strategy is the automatic curation and analysis of
                      electrochemical impedance spectroscopy (EIS) measurements,
                      using a specifically developed procedure in Python. The
                      varying quality of measurements from past years, as well as
                      recurring artefacts such as parasitic inductances, can be
                      addressed in this way. Additional distribution of relaxation
                      times (DRT) deconvolutions and equivalent circuit modelling
                      (ECM) are performed, as part of the procedure to
                      automatically retrieve feature values from measurements. The
                      novel dataset, which to the authors’ knowledge includes
                      some of the longest SOC stack tests available, serves as the
                      basis for several evaluations. In addition to classification
                      and clustering work to derive degradation patterns, in
                      particular based on the EIS data, another focus is on the
                      development of forecasting models. The current work is
                      primarily concerned with long short-term memory (LSTM), as
                      well as regression models that make use of both the time
                      series data and the characterisation measurements, such as
                      EIS. References:[1]: Jones, P.K., Stimming, U. $\&$ Lee,
                      A.A. Impedance-based forecasting of lithium-ion battery
                      performance amid uneven usage. Nat Commun 13, 4806 (2022).
                      [2]: B. Yang et al. Solid oxide fuel cell systems fault
                      diagnosis: Critical summarization, classification, and
                      perspectives. Journal of Energy Storage 34, 102153
                      (2021)[3]: A.K. Padinjarethil, S. Pollok $\&$ A. Hagen.
                      Degradation studies using machine learning on novel solid
                      oxide cell database. Fuel Cells. 21, 566–576 (2021)},
      month         = {May},
      date          = {2023-05-28},
      organization  = {18th International Symposium on Solid
                       Oxide Fuel Cells, Boston (USA), 28 May
                       2023 - 2 Jun 2023},
      subtyp        = {After Call},
      cin          = {IEK-9},
      cid          = {I:(DE-Juel1)IEK-9-20110218},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123) / HITEC -
                      Helmholtz Interdisciplinary Doctoral Training in Energy and
                      Climate Research (HITEC) (HITEC-20170406)},
      pid          = {G:(DE-HGF)POF4-1231 / G:(DE-Juel1)HITEC-20170406},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1019896},
}