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@ARTICLE{Wang:1024923,
      author       = {Wang, Qiao and Ye, Min and Cai, Xue and Sauer, Dirk Uwe and
                      Li, Weihan},
      title        = {{T}ransferable data-driven capacity estimation for
                      lithium-ion batteries with deep learning: {A} case study
                      from laboratory to field applications},
      journal      = {Applied energy},
      volume       = {350},
      issn         = {0306-2619},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-02573},
      pages        = {121747 -},
      year         = {2023},
      abstract     = {Capacity estimation plays a vital role in ensuring the
                      health and safety management of lithium-ion battery-based
                      electric-drive systems. This research focuses on developing
                      a transferable data-driven framework for accurately
                      estimating the capacity of lithium-ion batteries with the
                      same chemistry but different capacities in field
                      applications. The proposed approach leverages universal
                      information from a laboratory dataset and utilizes a
                      pre-trained network designed for small-capacity batteries
                      with constant-current discharging profiles. By applying this
                      framework, capacity estimation for large-capacity batteries
                      under drive cycles can be efficiently achieved with improved
                      performance. In addition, the incremental capacity analysis
                      is employed on two datasets, selecting a robust voltage
                      interval for health indicator extraction with physical
                      interpretations and uncertainty awareness of different fast
                      charging protocols. The feature extraction and dimension
                      increase processes are automated, utilizing the last short
                      charging sequences in wide voltage intervals while
                      considering the uncertainty related to various user charging
                      habits. Results demonstrate that the proposed strategy
                      significantly enhances both robustness and accuracy. When
                      compared to conventional methods, the proposed method
                      exhibits an average root mean square error improvement of
                      $68.40\%$ and $65.89\%$ in the best and worst cases,
                      respectively. The robustness of the proposed strategy is
                      further verified through 30 randomized health indicator
                      verifications. This research showcases the potential of
                      transferable deep learning in improving capacity estimation
                      by leveraging universal information for field applications.
                      The findings emphasize the importance of sharing knowledge
                      across different capacities of lithium-ion batteries,
                      enabling more effective and accurate capacity estimation
                      techniques.},
      cin          = {IEK-12},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-12-20141217},
      pnm          = {1223 - Batteries in Application (POF4-122) / BMBF 03XP0334
                      - Model2Life- Modellbasierte Systemauslegung für
                      2nd-Life-Nutzungsszenarien von mobilen Batteriesystemen
                      (03XP0334)},
      pid          = {G:(DE-HGF)POF4-1223 / G:(BMBF)03XP0334},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001061927700001},
      doi          = {10.1016/j.apenergy.2023.121747},
      url          = {https://juser.fz-juelich.de/record/1024923},
}