THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING ELECTRIC VEHICLE (EV) CHARGING INFRASTRUCTURE : A REVIEW
DOI:
https://doi.org/10.54489/ijcim.v4i2.414Keywords:
Electric Vehicle, Artificial Intelligence, Charging, infrastructure, Predictive AI, AlgorithmAbstract
With Electric Vehicles (EV) achieving higher adoption in the world, it becomes very necessary to have an EV charging infrastructure that is robust and intelligent and can adapt to changes per the number of EVs on our transportation grids. This paper presents a review of EV charging systems infrastructure and how Artificial Intelligence (AI) will help improve the EV charging grid, especially in terms of prediction ability and optimization benefits that AI can bring to our grid system. AI can dramatically enhance the user experience through predictive route planning, personal charging recommendations, and proactive maintenance alerts. AI-driven ADAS makes vehicles safer to operate and more efficient. Moreover, fog computing is intended to reduce latency and permit real-time data processing, enhancing responsiveness and reliability in the grid while recharging. Despite these benefits, the high costs, longer charging times, and inadequate charging infrastructure make EVs slow in adoption. This paper calls for relentless innovation in AI technologies and infrastructure to make this integration possible
References
eesi, “eesi.org. Retrieved from environmental and Energy Study Institute.” Accessed: Jun. 17, 2025. [Online]. Available: https://www.eesi.org/topics/fossil-fuels/description#:~:text=Fossil%20fuels%E2%80%94including%20coal%2C%20oil,were%20compressed%20and%20heated%20underground
E. Commission, “European Commission;. Retrieved from European Commission - Mobility and Transport.” Accessed: Jun. 17, 2025. [Online]. Available: https://transport.ec.europa.eu/facts-funding/studies-data/eu-transport-figures-statistical-pocketbook/statistical-pocketbook-2011_en
IEA..ORG, “IEA Global EV Outlook 2020,” IEA.ORG. Accessed: Jun. 17, 2025. [Online]. Available: https://www.iea.org/reports/global-ev-outlook-2020
K. Abnett, “https://www.weforum.org.” Accessed: Jun. 17, 2025. [Online]. Available: https://www.weforum.org/agenda/2022/07/eu-climate-change-policy-co2-fossil-fuels-2030-targets/
E. S. Rigas, S. D. Ramchurn, and N. Bassiliades, “Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, pp. 1619–1635, Aug. 2015, doi: 10.1109/TITS.2014.2376873. DOI: https://doi.org/10.1109/TITS.2014.2376873
J. A. Sanguesa, V. Torres-Sanz, P. Garrido, F. J. Martinez, and J. M. Marquez-Barja, “A Review on Electric Vehicles: Technologies and Challenges,” Smart Cities 2021, Vol. 4, Pages 372-404, vol. 4, no. 1, pp. 372–404, Mar. 2021, doi: 10.3390/SMARTCITIES4010022. DOI: https://doi.org/10.3390/smartcities4010022
O. A. Omitaomu and H. Niu, “Artificial Intelligence Techniques in Smart Grid: A Survey,” Smart Cities 2021, Vol. 4, Pages 548-568, vol. 4, no. 2, pp. 548–568, Apr. 2021, doi: 10.3390/SMARTCITIES4020029. DOI: https://doi.org/10.3390/smartcities4020029
L. K. Lam et al., “Advanced Metering Infrastructure for Electric Vehicle Charging,” Smart Grid and Renewable Energy, vol. 02, no. 04, pp. 312–323, 2011, doi: 10.4236/sgre.2011.24036. DOI: https://doi.org/10.4236/sgre.2011.24036
J. Bablo, “Electric Vehicle Infrastructure Standardization.”
S. Maase, X. Dilrosun, M. Kooi, and R. van den Hoed, “Performance of Electric Vehicle Charging Infrastructure: Development of an Assessment Platform Based on Charging Data,” World Electric Vehicle Journal 2018, Vol. 9, Page 25, vol. 9, no. 2, p. 25, Jul. 2018, doi: 10.3390/WEVJ9020025. DOI: https://doi.org/10.3390/wevj9020025
H. Altaleb and Z. Rajnai, “Electric Vehicle Charging Infrastructure and Charging Technologies,” Haditechnika, vol. 54, no. 4, pp. 8–12, 2020, doi: 10.23713/ht.54.4.03. DOI: https://doi.org/10.23713/HT.54.4.03
A. Gorbunova and I. Anisimov, “The analysis of the electric vehicle charging infrastructure in Tyumen city,” E3S Web of Conferences, vol. 164, p. 03016, May 2020, doi: 10.1051/E3SCONF/202016403016. DOI: https://doi.org/10.1051/e3sconf/202016403016
S. M. Varma, F. Castro, and S. T. Maguluri, “Electric Vehicle Fleet and Charging Infrastructure Planning,” Jun. 2023, Accessed: Jun. 17, 2025. [Online]. Available: https://arxiv.org/pdf/2306.10178 DOI: https://doi.org/10.2139/ssrn.4482128
S. M. Kandil, A. Abdelfatah, and M. A. Azzouz, “Optimization Approaches for Fast Charging Stations Allocation and Sizing: A Review,” IEEE Access, vol. 12, pp. 46741–46763, 2024, doi: 10.1109/ACCESS.2024.3381635. DOI: https://doi.org/10.1109/ACCESS.2024.3381635
P. Arévalo, D. Ochoa-Correa, and E. Villa-Ávila, “A Systematic Review on the Integration of Artificial Intelligence into Energy Management Systems for Electric Vehicles: Recent Advances and Future Perspectives,” Aug. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/wevj15080364. DOI: https://doi.org/10.3390/wevj15080364
D. Tranfield, D. Denyer, and P. Smart, “Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review,” British Journal of Management, vol. 14, no. 3, pp. 207–222, Sep. 2003, doi: 10.1111/1467-8551.00375. DOI: https://doi.org/10.1111/1467-8551.00375
Nick Zamanov, “cyberswitching,” cyberswitching.com. Accessed: Jun. 17, 2025. [Online]. Available: https://cyberswitching.com/the-role-of-ai-in-optimizing-electric-car-charging/
D. Weerdt et al., “Intention-aware routing to minimise delays at electric vehicle charging stations,” 2013. Accessed: Jun. 17, 2025. [Online]. Available: https://research.tue.nl/en/publications/intention-aware-routing-to-minimise-delays-at-electric-vehicle-ch-2 DOI: https://doi.org/10.1145/2516911.2516923
I. S. Bayram, G. Michailidis, M. Devetsikiotis, F. Granelli, and S. Bhattacharya, “Smart Vehicles in the Smart Grid: Challenges, Trends, and Application to the Design of Charging Stations,” in Control and Optimization Methods for Electric Smart Grids, A. Chakrabortty and M. D. Ilić, Eds., New York, NY: Springer New York, 2012, pp. 133–145. doi: 10.1007/978-1-4614-1605-0_6. DOI: https://doi.org/10.1007/978-1-4614-1605-0_6
B. Mo, Q. Y. Wang, J. Moody, Y. Shen, and J. Zhao, “Impacts of subjective evaluations and inertia from existing travel modes on adoption of autonomous mobility-on-demand,” Transp Res Part C Emerg Technol, vol. 130, p. 103281, Sep. 2021, doi: 10.1016/J.TRC.2021.103281. DOI: https://doi.org/10.1016/j.trc.2021.103281
M. M. Antony and R. Whenish, “Advanced Driver Assistance Systems (ADAS),” in Automotive Embedded Systems: Key Technologies, Innovations, and Applications, M. Kathiresh and R. Neelaveni, Eds., Cham: Springer International Publishing, 2021, pp. 165–181. doi: 10.1007/978-3-030-59897-6_9. DOI: https://doi.org/10.1007/978-3-030-59897-6_9
Switch.com, “The Power of AI for EV Charging Stations Planning.” Accessed: Jun. 18, 2025. [Online]. Available: https://getswitch.io/blog/ev-charging-stations-planning
scalecomputing, “scalecomputing,” scalecomputing.com. Accessed: Jun. 17, 2025. [Online]. Available: https://www.scalecomputing.com/resources/edge-computing-vs-fog-computing-vs-cloud-computing








