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001     1025014
005     20250203103324.0
024 7 _ |a 10.1016/j.energy.2023.126647
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024 7 _ |a 0360-5442
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024 7 _ |a 1873-6785
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024 7 _ |a WOS:000920218100001
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037 _ _ |a FZJ-2024-02608
082 _ _ |a 600
100 1 _ |a Li, Xiaohui
|0 0000-0002-5804-6656
|b 0
245 _ _ |a Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview
260 _ _ |a Amsterdam [u.a.]
|c 2023
|b Elsevier Science
336 7 _ |a article
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336 7 _ |a Journal Article
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500 _ _ |a Zudem unterstützt durch das Projekt: “GrEEn” (313-W044A)
520 _ _ |a 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.
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700 1 _ |a Wang, Zhenpo
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700 1 _ |a Zhang, Lei
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700 1 _ |a Sun, Fengchun
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700 1 _ |a Cui, Dingsong
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700 1 _ |a Hecht, Christopher
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700 1 _ |a Figgener, Jan
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700 1 _ |a Sauer, Dirk Uwe
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