Optimizing Electric Vehicle Charging Station Placement in Urban Areas: A Data-Driven Approach

This paper presents a new method for determining the best locations for electric vehicle charging stations in cities. The proposed optimization model uses data analysis and machine learning techniques to predict the demand for charging stations based on various factors, including driving patterns, population density, and the distribution of commercial and residential areas. An optimization algorithm then identifies the optimal placement of charging stations that can meet the predicted demand while minimizing infrastructure costs. Simulation studies demonstrate that the proposed model provides a more efficient and cost-effective deployment of charging stations when compared to existing approaches. This practical and innovative solution can inform decision-making processes for charging station deployment, reduce the cost and complexity of charging infrastructure installation. The proposed data-driven approach can provide valuable insights for policymakers, helping to guide infrastructure deployment and reduce costs. It also advances the state of the art in electrical and electronics engineering, introducing a novel and practical method to optimize EV charging infrastructure deployment in urban areas