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@ARTICLE{Steininger:1024897,
      author       = {Steininger, Valentin and Rumpf, Katharina and Hüsson,
                      Peter and Li, Weihan and Sauer, Dirk Uwe},
      title        = {{A}utomated feature extraction to integrate field and
                      laboratory data for aging diagnosis of automotive
                      lithium-ion batteries},
      journal      = {Cell reports / Physical science},
      volume       = {4},
      number       = {10},
      issn         = {2666-3864},
      address      = {[New York, NY]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2024-02548},
      pages        = {101596 -},
      year         = {2023},
      note         = {Unterstützt durch BMWK Grant ‘‘COBALT-P’’
                      (16BZF314C)},
      abstract     = {Battery aging diagnosis using field data readouts presents
                      distinct challenges compared with using laboratory data.
                      These challenges stem from the complexity of the data
                      structure and potential inconsistencies in aging values
                      obtained from variations in battery management system
                      software versions. Consequently, the efficacy of a
                      data-driven approach to identify pertinent aging features
                      from field data becomes susceptible to these factors. In
                      this work, we investigate different feature extraction
                      methods and propose a framework designed to mitigate issues
                      arising from compromised data quality. For this purpose, we
                      leverage the benefits of precise laboratory aging data
                      alongside authentic driving data acquired from a cohort
                      exceeding 600,000 customers to improve the aging diagnosis
                      of vehicle batteries. Moreover, we provide functional
                      fitting of statistical data, addressing the challenges posed
                      by incomplete data structures. We validate our methods by
                      comparing them with state-of-the-art feature extraction
                      techniques, yielding a $57\%$ enhancement in aging
                      estimation accuracy.},
      cin          = {IEK-12},
      ddc          = {530},
      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:001114813200001},
      doi          = {10.1016/j.xcrp.2023.101596},
      url          = {https://juser.fz-juelich.de/record/1024897},
}