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@ARTICLE{Wang:1024892,
      author       = {Wang, Qiushi and Wang, Zhenpo and Liu, Peng and Zhang, Lei
                      and Sauer, Dirk Uwe and Li, Weihan},
      title        = {{L}arge-scale field data-based battery aging prediction
                      driven by statistical features and machine learning},
      journal      = {Cell reports / Physical science},
      volume       = {4},
      number       = {12},
      issn         = {2666-3864},
      address      = {[New York, NY]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2024-02543},
      pages        = {101720 -},
      year         = {2023},
      note         = {Unterstützt durch das Projekt ‘‘COBALT-P’’
                      (16BZF314C) des BMKW},
      abstract     = {Accurately predicting battery aging is critical for
                      mitigating performance degradation during battery usage.
                      While the automotive industry recognizes the importance of
                      utilizing field data for battery performance evaluation and
                      optimization, its practical implementation faces challenges
                      in data collection and the lack of field data-based
                      prognosis methods. To address this, we collect field data
                      from 60 electric vehicles operated for over 4 years and
                      develop a robust data-driven approach for lithium-ion
                      battery aging prediction based on statistical features. The
                      proposed pre-processing methods integrate data cleaning,
                      transformation, and reconstruction. In addition, we
                      introduce multi-level screening techniques to extract
                      statistical features from historical usage behavior.
                      Utilizing machine learning, we accurately predict aging
                      trajectories and worst-lifetime batteries while quantifying
                      prediction uncertainty. This research emphasizes a field
                      data-based framework for battery health management, which
                      not only provides a vital basis for onboard health
                      monitoring and prognosis but also paves the way for battery
                      second-life evaluation scenarios.},
      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:001144156000001},
      doi          = {10.1016/j.xcrp.2023.101720},
      url          = {https://juser.fz-juelich.de/record/1024892},
}