Optimizing Electric Vehicle Charging Networks Using Clustering Technique
A novel way for EV charging is presented by establishing a smart charging hub network using dynamic K-means clustering and a synchronized hub network that employs a novel clustering method, organizes charging nodes, predicts demand via predictive analytics, employs RES, and encourages consumers to use environmentally conscious charging behaviours.
Abstract
The increasing use of electric vehicles (EVs) highlights the need of a designed effectively charging network. This paper presents a novel way for EV charging by establishing a smart charging hub network using dynamic K-means clustering. The unpredictable nature of EV use patterns makes static clustering systems difficult to maintain up, which leads to inefficiency and load at charging points. The failure of current electric vehicle charging facilities to adapt to altering use patterns leads in unsatisfactory charging experiences for customers. These issues are exacerbated by charging station load mismatches and an inability to connect with renewable energy sources (RES). This paper attempts to address these issues by establishing a dynamic clustering approach and a synchronized hub network. The most important outcome is intelligent charging hub equipment that can quickly react to the specific charging demands of each specific EV. The system that is suggested employs a novel clustering method, organizes charging nodes, predicts demand via predictive analytics, employs RES, and encourages consumers to use environmentally conscious charging behaviours. Insights into the development of electric transportation are provided, which lays out a detailed plan for building a smart and environmentally friendly EV charging network.