Electric Vehicle Grid Integration: A Review of Methods and Challenges
DOI:
https://doi.org/10.61705/rrb9s232Keywords:
Electric Vehicles, Charging Behavior, Grid In-tegration, Load Management, Renewable Energy Integration, Smart Charging, Vehicle-to-Grid, Voltage StabilityAbstract
This paper presents a comprehensive review of electric vehicle (EV) charging behavior and its impact on power grid integration. With the rapid growth of EV adoption driven by environmental concerns and advancements in battery tech-nologies understanding charging demand patterns has become essential for efficient energy management. The study employs a systematic literature review approach using the PRISMA framework analyzing 20 high quality research papers selected from an initial pool of 200 studies. It explores key aspects such as EV charging technologies, user behavior, temporal and spatial charging patterns and their implications on grid stability. The findings highlight that EV charging demand is highly dynamic and influenced by factors such as user routines, charging infrastructure, pricing mechanisms and battery state of charge. Uncoordinated charging can lead to peak load issues, voltage instability and infrastructure stress, whereas smart charging and vehicle to grid (V2G) technologies can enhance grid reliability and efficiency. Additionally the study identifies emerging trends such as renewable energy integration, machine learning based forecasting and advanced optimization techniques. Overall the paper provides valuable insights into current research trends, challenges as well as opportunities for sustainable EV grid integration and efficient charging management.
References
R. Gunasekaran et al., “Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convo-lutional network,” Discover Applied Sciences, vol. 6, no. 11, p. 605, 2024.
Y. Chen et al., “Electric vehicle charging demand prediction model based on spatiotemporal attention mechanism,” Energies, vol. 18, no. 3, p. 687, 2025.
K. Clement-Nyns, E. Haesen, and J. Driesen, “The impact of charging plug-in hybrid electric vehicles on a residential distribution grid,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 371–380, Feb. 2010, doi: 10.1109/TPWRS.2009.2036481.
P. Chen et al., “Electric vehicle charging demand forecasting at charging stations under climate influence for electricity dispatching,” IET Power Electronics, vol. 18, no. 1, p. e12833, 2025.
A. Mystakidis et al., “EV charging forecasting exploiting traffic, weather and user information,” International Journal of Machine Learning and Cybernetics, pp. 1–27, 2025.
S. Sanami et al., “Demand forecasting for electric vehicle charg-ing stations using multivariate time-series analysis,” arXiv preprint arXiv:2502.16365, 2025.
Z. Yi et al., “Electric vehicle charging demand forecasting using deep learning model,” Journal of Intelligent Transportation Systems, vol. 26, no. 6, pp. 690–703, 2022.
N. Shaukat et al., “A survey on electric vehicle transportation within smart grid system,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 1329–1349, Jan. 2018, doi: 10.1016/j.rser.2017.05.092.
J. Morfeldt, S. D. Kurland, and D. J. A. Johansson, “Carbon footprint impacts of banning cars with internal combustion engines,” Transporta-tion Research Part D: Transport and Environment, vol. 95, p. 102807, 2021, doi: 10.1016/j.trd.2021.102807.
K. Forrest et al., “Estimating the technical feasibility of fuel cell and battery electric vehicles for the medium and heavy duty sec-tors in California,” Applied Energy, vol. 276, p. 115439, 2020, doi: 10.1016/j.apenergy.2020.115439.
J. Leijon and C. Bostro¨m, “Charging electric vehicles today and in the future,” World Electric Vehicle Journal, vol. 13, no. 8, p. 139, 2022.
P. Y. Kong, “Extending energy storage lifetime of autonomous robot-like mobile charger for electric vehicles,” IEEE Access, vol. 8, pp. 106811–106821, 2020, doi: 10.1109/ACCESS.2020.3000021.
Z. J. Lee, T. Li, and S. H. Low, “ACN-Data: Analysis and applications of an open EV charging dataset,” in Proc. ACM International Conference on Future Energy Systems, Phoenix, AZ, USA, 2019, pp. 139–149.
H. S. Das et al., “Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review,” Renewable and Sustainable Energy Reviews, vol. 120, p. 109618, 2020, doi: 10.1016/j.rser.2019.109618.
R. Challa, D. Kamath, and A. Anctil, “Well-to-wheel greenhouse gas emissions of electric versus combustion vehicles from 2018 to 2030 in the US,” Journal of Environmental Management, vol. 308, p. 114592, 2022, doi: 10.1016/j.jenvman.2022.114592.
M. Gustafsson, N. Svensson, M. Eklund, and B. F. Mo¨ller, “Well-to-wheel climate performance of gas and electric vehicles in Europe,” Transportation Research Part D: Transport and Environment, vol. 97, p. 102911, 2021, doi: 10.1016/j.trd.2021.102911.
H. Krueger and A. Cruden, “Integration of electric vehicle user charging preferences into vehicle-to-grid aggregator controls,” Energy Reports, vol. 6, pp. 86–95, 2020, doi: 10.1016/j.egyr.2020.11.052.
K. Csillak and S. Kamenz, “Electric charging infrastructure and gender equality: An overview for USER-CHI (H2020 project),” Open Research Europe, vol. 3, p. 47, 2023.
T. R. McKinney, E. E. F. Ballantyne, and D. A. Stone, “Rural EV charging: The effects of charging behaviour and electricity tariffs,” Energy Reports, vol. 9, pp. 2321–2334, 2023.
Z. J. Lee, T. Li, and S. H. Low, “ACN-Data: Analysis and applications of an open EV charging dataset,” in Proc. ACM International Conference on Future Energy Systems, Phoenix, AZ, USA, 2019, pp. 139–149.
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