Optimal Site Selection Planning of EV Charging Pile Based on Genetic Algorithm

• Published in: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) pp. 1799 - 1803
• Main Authors: He, Jiemeng, Zhang, Lin, Hu, Rongfang, Xie, Yuande
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2020
• Subjects: Analytical models; Automobiles; charging pile; Electric vehicle charging; genetic algorithm; Linear programming; multiobjective planning; optimal location; Planning; Predictive models
• DOI: 10.1109/ITOEC49072.2020.9141690

With the aggravation of environmental pollution problems, electric vehicles have attracted the attention of the governments of all countries for their advantages of zero pollution and zero emissions. Reasonable charging facilities are of great significance to the development of electric vehicle industry. By predicting the current EV ownership, predicting EV charging demand and analyzing various influencing factors of charging pile construction, an optimal site selection model for multi-objective planning of EV charging pile is established to improve charging efficiency of EV. The genetic algorithm is used to solve the model, and through example analysis, the time cost of planning candidate points from charging demand points of electric vehicles to candidate points of charging piles are obtained, and the optimal site selection result of charging piles is given. This model can provide a reference for the rational planning of charging piles in the future.