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@ARTICLE{Li:998573,
      author       = {Li, Weihan and Chen, Jue and Quade, Katharina and Luder,
                      Daniel and Gong, Jingyu and Sauer, Dirk Uwe},
      title        = {{B}attery degradation diagnosis with field data,
                      impedance-based modeling and artificial intelligence},
      journal      = {Energy storage materials},
      volume       = {53},
      issn         = {2405-8289},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {FZJ-2023-01186},
      pages        = {391 - 403},
      year         = {2022},
      abstract     = {By collecting battery data from the field and building up
                      the battery digital twin in the cloud, the degradation of
                      batteries can be monitored online on the electrode level and
                      the information regarding the degradation modes can be
                      extracted from the data. Here, we present a degradation
                      diagnosis framework for lithium-ion batteries by integrating
                      field data, impedance-based modeling, and artificial
                      intelligence, revolutionizing the degradation identification
                      with accurate and robust estimation of both capacity and
                      power fade together with degradation mode analysis. By
                      integrating an impedance-based model and an open-circuit
                      voltage reconstruction model, the hybrid model consists of
                      parameters representing the change of impedance in a wide
                      frequency domain and the change of open-circuit voltage
                      during degradation. Based on the field data with low and
                      high dynamics, the data-driven parameter identification
                      method using a multi-step cuckoo search algorithm
                      considering parameter sensitivity differences shows high
                      accuracy and robustness in aging parameter estimation and
                      degradation mode identification even under sensor noise.
                      Furthermore, the data requirement for the battery digital
                      twin in the sense of sampling rate was investigated
                      considering degradation identification accuracy,
                      computational cost, and data storage cost. This work
                      highlights the opportunity in online electrode-level
                      degradation diagnosis in the field through battery modeling
                      and artificial intelligence.},
      cin          = {IEK-12},
      ddc          = {624},
      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:000862781600002},
      doi          = {10.1016/j.ensm.2022.08.021},
      url          = {https://juser.fz-juelich.de/record/998573},
}