Work

Multi-Objective Genetic Algorithm for Charging Station Capacity and Location Optimization

Year: 2025

Type: article

Abstract: This paper presents a multi-objective optimization model for determining charging station capacity and location, aiming to maximize revenue, minimize waiting probability, and reduce construction and maintenance costs. It uses a genetic algorithm to balance these objectives, ensuring practicality and efficiency. The model also incorporates maximizing the coverage area for location simulation. Applied to Shanghai's Pudong New Area, the region is divided into sub-districts, and necessary parameters are determined. The proposed algorithm plans and sites charging and battery-swapping stations, determining their layout and quantity. This provides practical references for planning and constructing new energy vehicle charging infrastructure, thereby enhancing the accessibility and efficiency of charging facilities. The research provides a scientific foundation for optimizing electric vehicle charging infrastructure, promoting the sustainable development of the electric vehicle ecosystem, and facilitating the widespread adoption of electric vehicles.

Source: International Journal of Intelligent Information Technologies

DOI: 10.4018/ijiit.381093

Authors: Yixuan Wang, Jinghua Zhao, Han Wen

Institutions: University of Shanghai for Science and Technology, Guizhou University of Finance and Economics

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Affordable and clean energy

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Education and Scientific Research Project of Shanghai