Integration of electric vehicles into energy system models: A review

Abstract

The growing adoption of electric vehicles (EVs) presents challenges and opportunities for the energy system, making accurate modeling of EV charging demand and its flexibility essential. EVs create a critical link between the electricity and transport sectors, both requiring models with high temporal resolution. Our review examines methods for modeling EV demand and their integration into energy system models across spatial and temporal scales. We identified 58 EV demand models and 32 integration studies spanning building- to international-scale systems. EV demand models are classified according to their methodological approaches and data sources. Our review highlights that activity-based approaches relying on travel or time use surveys are particularly effective in representing charging flexibility, as they derive empirically based flexibility windows from observed behavior. However, current approaches, mainly based on Markov chains and Monte Carlo simulations, fail to reproduce realistic weekly and seasonal patterns. Advances in generative modeling, especially through deep neural networks, show promise in capturing temporal consistency and variability, as well as integrating complementary data sources. For the integration of EVs into energy system models, we identify diverse aggregation methods, each entailing trade-offs between behavioral realism and computational tractability, yet systematic benchmarking is lacking. Our review reveals a lack of spatially fine-grained, high-fidelity, and diverse models, which are particularly important for analyzing innovative mobility concepts and their impacts on distribution grids. Future high-fidelity EV demand models are essential for anticipating future demand patterns, assessing system flexibility, and guiding infrastructure planning and investment. • We review 58 electric vehicle demand models and 32 integration studies (2010–2025). • We classify electric vehicle demand models by design and data characteristics. • We review energy system models from building scale to the international level. • We compare five electric vehicle aggregation methods and assess their trade-offs. • We identify a lack of spatially fine-grained, high-fidelity, and diverse models.