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@ARTICLE{Li:1025014,
      author       = {Li, Xiaohui and Wang, Zhenpo and Zhang, Lei and Sun,
                      Fengchun and Cui, Dingsong and Hecht, Christopher and
                      Figgener, Jan and Sauer, Dirk Uwe},
      title        = {{E}lectric vehicle behavior modeling and applications in
                      vehicle-grid integration: {A}n overview},
      journal      = {Energy},
      volume       = {268},
      issn         = {0360-5442},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-02608},
      pages        = {126647 -},
      year         = {2023},
      note         = {Zudem unterstützt durch das Projekt: “GrEEn”
                      (313-W044A)},
      abstract     = {The increasing electric vehicle (EV) adoption in the
                      context of transportation electrification and carbon
                      neutrality pursuit brings both new challenges and
                      opportunities for all the stakeholders in EV-grid
                      integration. To fully unleash the potentials of EVs as
                      flexible distributed energy storage to facilitate efficient
                      EV-grid interactions, it is imperative to predict
                      spatio-temporal distributions of EV charging demand,
                      optimize charging infrastructure layout and implement smart
                      charging scheduling schemes. Appropriate EV behavior
                      modeling plays a fundamental role to realize these targets.
                      This paper aims to provide a comprehensive review on EV
                      behavior modeling and its applications in EV-grid
                      integration algorithm development. Various models have been
                      developed to describe EV usage pattern, charging decision
                      making process and response to smart charging schemes. In
                      particular, the existing usage pattern models including
                      temporal, spatial and energy sub-models are expounded, and
                      different sub-models of charging choice and response to
                      smart charging are also presented. An EV behavior modeling
                      paradigm is proposed to provide guidance for EV behavior
                      model selection in different application scenarios by
                      developing different portfolios of temporal, spatial, energy
                      usage, charging choice and response models. Accordingly,
                      enabling EV behavior modeling for EV charging demands
                      prediction and smart charging scheduling is covered in
                      details. This study provides in-depth behavioral insights
                      and viable approaches to developing efficient EV behavior
                      models for advancing EV-grid integration and provides
                      perspectives towards future research directions.},
      cin          = {IEK-12},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IEK-12-20141217},
      pnm          = {1221 - Fundamentals and Materials (POF4-122)},
      pid          = {G:(DE-HGF)POF4-1221},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000920218100001},
      doi          = {10.1016/j.energy.2023.126647},
      url          = {https://juser.fz-juelich.de/record/1025014},
}