Electric Vehicle Charging Network Planning Based on Multi-Objective Optimization and Real-Time Data Analysis
DOI:
https://doi.org/10.13052/dgaej2156-3306.405617Keywords:
Multi-objective optimization algorithms, real-time data analysis, charging network, graph neural networks, network planningAbstract
Current Electric Vehicle network planning methods have shortcomings in technical standards and layout structures, often failing to consider regional differences and user needs, leading to irrational charging network layouts. This study integrates Multi-Objective Optimization Algorithms with real-time data and employs Graph Neural Networks for dynamic adjustments and optimization of charging strategies. Results show that with a maximum of three user attempts, the proposed framework achieves a total cost of 3.37 × 106 USD, lower than Moth-Flame Optimization (4.39 × 106 USD), Monte Carlo (4.57 × 106 USD), and Fuzzy Multi-objective Optimization (5.42 × 106 USD). When the degree of aggregation reaches high aggregation, the average waiting time of the research architecture is the lowest, at 3.32ta/min. The algorithm optimizes charging resource allocation, enhances charging station efficiency, and improves Electric Vehicle network planning, making it a valuable contribution to intelligent transportation systems.
Downloads
References
QuHaohao, KuangHaoxuan, WangQiuxuan, LiJun, YouLinlin. A Physics-Informed and Attention-Based Graph Learning Approach for Regional Electric Vehicle Charging Demand Prediction. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(10): 14284–14297.
Chen L, He L, Zhou Y. An Exponential Cone Programming Approach for Managing Electric Vehicle Charging. Operations Research, 2024, 72(5): 2215–2240.
Ullah I, Zheng J, Jamal A, Zahid M, Almoshageh M, Safdar M. Electric vehicles charging infrastructure planning: a review. International Journal of Green Energy, 2024, 21(7): 1710–1728.
Kmay O B, Gzara F, Alumur S A. Charging Station Location and Sizing for Electric Vehicles Under Congestion. Transportation science, 2023, 57(6): 1433–1451.
Alansari M, Al-Sumaiti A S, Abughali A. Optimal placement of electric vehicle charging infrastructures utilizing deep learning. IET intelligent transport systems, 2024, 18(8): 1529–1544.
Cao G, Sun G, Yuan S, Yue Y, Wu Y. Multiobjective Optimization of Spiral Guide Vanes for Boosting Separation Performance of Cyclone Separators. Industrial & Engineering Chemistry Research, 2024, 63(20): 9162–9175.
Xu T, Luo J, He X, Li C, Zhang X. Wave aberration corrections in PSF ellipticity measurements of astronomical telescopes using a multi-objective optimization. Applied Optics, 2024, 63(3): 743–756.
Kermani A, Ehsani A. Multi-objective optimization of a sandwich structure with a hybrid composite grid core. Advances in Structural Engineering, 2023, 26(1): 137–152.
Liu J, Liu Y. High-Throughput Real-Time Reliable Data Collection in Wireless Sensor Network: Implementation and Analysis. IEEE sensors journal, 2024, 24(17): 28251–28266.
Woo-Kyun Jung, Song Y, Suh E S. Garment production line optimization using production information based on real-time power monitoring data. Systems engineering, 2024, 27(2): 338–353.
Kathirvel K, Ragavan S, Balashanmugham A, Sundaram M G. Hybrid methodology-based energy management of microgrid with grid-isolated electric vehicle charging system in smart distribution network. Electrical engineering, 2024, 106(3): 2705–2720.
Fareed A, Mohd B. Allocation of plug-in electric vehicle charging station with integrated solar powered distributed generation using an adaptive particle swarm optimization. Electrical engineering, 2024, 106(3): 2595–2608.
Arjun V, Selvan M P. Assessing the need for network-based technical constraints in economic optimization of electric vehicle charging. Electrical engineering, 2023, 105(3): 1629–1641.
Huang N, Hu L, Wang R, Cai G, Guo Y, Zhao X. Multi-Objective Optimal Scheduling of Distribution Network with Electric Vehicle Charging Load Considering Time-Varying Road Impedance. Journal of Electrical Engineering & Technology, 2023, 18(4): 2667–2681.
Ferraz R S F, Ferraz R S F, Medina, Augusto C. Rueda C. Fardin, Jussara F. F. Multi-objective approach for optimized planning of electric vehicle charging stations and distributed energy resources. Electrical engineering, 2023, 105(6): 4105–4117.
Chen H, Niu S, Shao Z, Jian L. Recognizing Multitype Misalignments in Wireless EV Chargers with Orientation-Sensitive Coils: A Data-Driven Strategy Using Improved ResNet. IEEE transactions on industrial informatics, 2024, 20(1): 280–290.
Shweta M, Anoop A. Optimal planning of power distribution system employing electric vehicle charging stations and distributed generators using metaheuristic algorithm. Electrical engineering, 2024, 106(2): 1373–1389.
Elkasrawy M A, Abdellatif S O, Ebrahim G A, Ghali H A. Real-time optimization in electric vehicle stations using artificial neural networks. Electrical Engineering, 2023, 105(1): 79–89.
Zhang Z, Liu S, Xiong Q, Liu Y. Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells. The Journal of Physical Chemistry Letters, 2025, 16(3): 738–746.
Zhang J, Jing W, Lu Z, Wu H, Wen X. Collaborative strategy for electric vehicle charging scheduling and route planning. IET Smart Grid, 2024, 7(5): 628–642.
Jiahuan Wang, Haixiao Jia, Xuejiao Bai, et al. Research on The Location of Railway Train in Tunnel Based on Factor Graph Optimization[J]. Applied Computer Letters, 2023, 7(1), pp. 5–10.
Jiahuan Wang, Haixiao Jia, Peifen Pan, et al. Research on The Technology of Man-Machine Collision Early Warning System in Tunnels Based on Bds High-Precision Positioning in Tunnel[J]. Applied Computer Letters, 2023, 7(1).
