Electric Vehicle Behavior Modeling for Vehicle-to-Grid Integration: Methods, Challenges, and Perspectives

The widespread adoption of electric vehicles (EVs) presents significant opportunities and challenges for power systems, especially in Vehicle-to-Grid (V2G) integration. Accurate modeling of EV charging and mobility behaviour is therefore crucial for enabling reliable and efficient V2G operation. This paper reviews current paradigms of EV behavior modeling, including statistical, data-driven, and decision-oriented approaches, and compares them from a V2G-service-oriented rather than purely algorithm-centric perspective. The analysis focuses on modeling assumptions, data requirements, computational characteristics, and their suitability for different V2G tasks and decision layers. Key challenges are identified, including data availability and heterogeneity, limited cross-scenario generalizability, insufficient integration of physical and behavioral constraints, and computational barriers to large-scale and real-time deployment. To address these limitations, this paper introduces a Task–Data–Deployment perspective framework, which emphasizes aligning modeling paradigms with specific V2G tasks, realistic data conditions, and deployment feasibility. Rather than proposing new algorithms, this perspective provides practical guidance for selecting and applying EV behavior models in real-world V2G systems. These insights clarify current gaps between modeling research and deployment needs, and support the development of scalable, transferable, and operationally viable EV behavior modeling frameworks for future large-scale V2G integration.