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@ARTICLE{Li:910839,
      author       = {Li, Weihan and Demir, Iskender and Cao, Decheng and Jöst,
                      Dominik and Ringbeck, Florian and Junker, Mark and Sauer,
                      Dirk Uwe},
      title        = {{D}ata-driven systematic parameter identification of an
                      electrochemical model for lithium-ion batteries with
                      artificial intelligence},
      journal      = {Energy storage materials},
      volume       = {44},
      issn         = {2405-8289},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {FZJ-2022-04189},
      pages        = {557 - 570},
      year         = {2022},
      abstract     = {Electrochemical models are more and more widely applied in
                      battery diagnostics, prognostics and fast charging control,
                      considering their high fidelity, high extrapolability and
                      physical interpretability. However, parameter identification
                      of electrochemical models is challenging due to the
                      complicated model structure and a large number of physical
                      parameters with different identifiability. The scope of this
                      work is the development of a data-driven parameter
                      identification framework for electrochemical models for
                      lithium-ion batteries in real-world operations with
                      artificial intelligence, i.e., the cuckoo search algorithm.
                      Only current and voltage data are used as input for the
                      multi-objective global optimization of the parameters
                      considering both voltage error between the model and the
                      battery and the relative capacity error between two
                      electrodes. The multi-step identification process based on
                      sensitivity analysis increases the identification accuracy
                      of the parameters with low sensitivity. Moreover, the novel
                      identification process inspired by the training process in
                      machine learning further overcomes the overfitting problem
                      using limited battery data. The data-driven approach
                      achieves a maximum root mean square error of 9 mV and 12.7
                      mV for the full cell voltage under constant current
                      discharging and real-world driving cycles, respectively,
                      which is only $17.9\%$ and $42.9\%$ of that of the
                      experimental identification approach.},
      cin          = {IEK-12 / JARA-ENERGY},
      ddc          = {624},
      cid          = {I:(DE-Juel1)IEK-12-20141217 / $I:(DE-82)080011_20140620$},
      pnm          = {1223 - Batteries in Application (POF4-122) / EVERLASTING -
                      Electric Vehicle Enhanced Range, Lifetime And Safety Through
                      INGenious battery management (713771) / BMBF 03XP0334 -
                      Model2Life- Modellbasierte Systemauslegung für
                      2nd-Life-Nutzungsszenarien von mobilen Batteriesystemen
                      (03XP0334)},
      pid          = {G:(DE-HGF)POF4-1223 / G:(EU-Grant)713771 /
                      G:(BMBF)03XP0334},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000778692600001},
      doi          = {10.1016/j.ensm.2021.10.023},
      url          = {https://juser.fz-juelich.de/record/910839},
}