Analysis of Enhancing EV Charging Efficiency in the 5G enabled Smart Grid using Network Slicing, Edge Computing, and AI-based EV Charging Prediction |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-7 |
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| Year of Publication : 2023 | ||
| Authors : Sathiyakeerthi Madasamy | ||
| DOI : 10.14445/22312803/IJCTT-V71I7P101 | ||
How to Cite?
Sathiyakeerthi Madasamy, "Analysis of Enhancing EV Charging Efficiency in the 5G enabled Smart Grid using Network Slicing, Edge Computing, and AI-based EV Charging Prediction," International Journal of Computer Trends and Technology, vol. 71, no. 7, pp. 1-7, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I7P101
Abstract
As both electricity infrastructures and EVs have become more commonplace, there has been a lot of interest in vehicle-to-grid (V2G) technology as a possible energy management solution in the 5G smart grid. Due to EVs' high mobility and low reliability, the smart grid has a hard time keeping up with the demand for on-demand services. Since this is the case, we propose a new smart grid architecture for the 5G smart grid that incorporates network slicing and edge computing. We can improve the efficiency of EV charging and reduce costs for energy service providers by tracking the two-way traffic between EVs and smart grids. In order to maximize the effectiveness of EV scheduling, accurate predictions of EV charging behaviours are a significant problem for V2G systems. In this study, we provide a hybrid AI-based approach to estimating how often EV owners will plug into their vehicles. Simulation findings show that the proposed prediction system provides more accurate forecasts and more efficient scheduling for EV charging than existing leading methods.
Keywords
Electric Vehicle, Charging station, 5 G technology, Wireless communication, Energy.
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