Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66261
Volume:
10 |
7 |
Jul 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science
City Engineering College
Department of Computer Science
City Engineering College
Department of Computer Science
City Engineering College
The growth of electric vehicles (EVs) is limited by the inherent lack of dispersed charging infrastructure, resulting in service gaps both within dense urban centers and in remote rural communities. This research seeks to overcome these obstacles by creating a dynamic approach that will embed mobility into the transit system with mobile charging stations. While the addition of roaming charging stations/ roadside assist vehicles increases mobility, they are only worthwhile if put into practice efficiently. To this end, to fully exploit our charging infrastructure, and minimize vehicle charging time, we propose an optimal network siting algorithm model. Utilizing traffic volume and local power grid constraints and geographic information this model will produce a mathematical optimization algorithm that will find priority locations for new deployments. By treating charging availability as an open access moving service, not a fixed asset, this model fills the space between the desired charging location (which may not be economically viable, or may not have infrastructure nearby) and existing, stable locations for our chargers. Data shows the deployment of a mobile/ traveling charger program coupled with the optimal siting model will diminish drivers' stress levels, limit the amount of capital spent on building infrastructure, and bring EV technology within reach of new populations currently unreached by existing infrastructure.
KEYWORDS
This paper successfully demonstrates the implementation of a high-performance mobile application designed to improve the scalability of modern electric vehicle charging networks. By replacing inefficient manual allocation methods with an automated framework that syncs geographic positions and port vacancy metrics, the platform resolves critical information gaps for EV drivers. Comprehensive empirical evaluations verify that the decoupled software design provides fast execution speeds, high security, and cross-platform compatibility. This optimization maximizes station utilization rates and minimizes range anxiety, presenting a practical template for sustainable smart city planning.
hen, S., & Tong, L. (2012). iEMS for large scale charging of electric vehicles: Architecture and optimal online scheduling. Proceedings of IEEE International Conference on Smart Grid Communications, Tainan City, Taiwan, 214–219.Chen, N., Quek, T. Q. S., & Tan, C. W. (2012). Optimal charging of electric vehicles in smart grid: Characterization and valley-filling algorithms. Proceedings of IEEE International Conference on Smart Grid Communications, Tainan City, Taiwan, 342–347.Lam, A. Y. S., Huang, L., Silva, A., & Saad, W. (2012). A multi-layer market for vehicle-to-grid energy trading in the smart grid. Proceedings of 1st IEEE INFOCOM Workshop on Green Networking and Smart Grids, Orlando, FL, 18–23.Lam, A. Y. S., K.-C. Leung, & Li, V. O. K. (2012). Capacity management of vehicle-to-grid system for power regulation services. Proceedings of IEEE International Conference on Smart Grid Communications, Tainan City, Taiwan, 520–525.Yu, J. J. Q., Li, V. O. K., & Lam, A. Y. S. (2013). Optimal V2G scheduling of electric vehicles and unit commitment using chemical reaction optimization. Proceedings of IEEE Congress on Evolutionary Computation, Cancun, Mexico, 1142–1149.Guo, F., Inoa, E., Choi, W., & Wang, J. (2012). Study on global optimization and control strategy development for a PHEV charging facility. IEEE Transactions on Vehicular Technology, 61(6), 2431–2441.Etezadi-Amoli, M., Choma, K., & Stefani, J. (2010). Rapid-charge electric vehicle stations. IEEE Transactions on Power Delivery, 25(3), 1883–1887.Masoum, A., Deilami, S., Moses, P., Masoum, M., & Abu-Siada, A. (2011). Smart load management of plug-in electric vehicles in distribution and residential networks with charging stations for peak shaving and loss minimisation considering voltage regulation. IET Generation, Transmission & Distribution, 5(8), 877–888.Liu, Z., Wen, F., & Ledwich, G. (2013). Optimal planning of electric-vehicle charging stations in distribution systems. IEEE Transactions on Power Delivery, 28(1), 102–110.Liu, Z. F., Zhang, W., Ji, X., & Ke, L. (2012). Optimal planning of charging station for electric vehicle based on particle swarm optimization. Proceedings of IEEE Innovative Smart Grid Technologies - Asia, Tianjin, China, 451–456.
Publication Date
7/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...