Electric vehicles (EVs) play a crucial role in tackling environmental issues in the transportation industry. The incorporation of effective charging infrastructure is crucial in promoting the broad acceptance of electric vehicles (EVs). This work investigates the optimization of the location of wireless charging infrastructure in urban contexts using genetic algorithms (GAs). The location data, which includes latitude and longitude coordinates, showed a wide range of spatial distributions that are ideal for deploying charging stations. These distributions display variances that are favorable for strategically placing the infrastructure. The examination of power consumption data revealed significant variations in energy demand across different sites, ranging from 180 kWh to 300 kWh. These differences indicate that each location has its own distinct energy needs. The population density statistics exhibited a spectrum of values, ranging from 600 individuals per square unit. The population density is 1200 persons per square kilometer. The abbreviation "km" refers to kilometers, which is used to indicate different levels of prospective electric vehicle (EV) users. In addition, the distance data provided information about the lengths between prospective locations for charging stations, which varied from 400 km to 1200 km. These distances had an impact on the concerns of connection and transmission efficiency. The research highlights the intricate nature of the elements that affect the ideal location of infrastructure, underlining the need for a methodical approach to optimization. Integrating these statistics provides a foundation for developing an objective function in the GA framework to optimize the location of charging infrastructure. The study's results provide valuable understanding of the many factors that influence the location of charging infrastructure. The goal is to promote the development of efficient and easily accessible electric vehicle charging networks in metropolitan areas.
