Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms

Route planning for electric vehicles (EVs) is a critical challenge in sustainable transportation, as it directly addresses concerns about greenhouse gas emissions and energy efficiency. This study presents a novel approach that combines K-means clustering and GA optimization to create dynamic, real-world applicable routing solutions. This framework incorporates practical challenges, such as charging station queue lengths, which significantly influence travel time and energy consumption. Using K-means clustering, the methodology groups charging stations based on geographical proximity, allowing for optimal stop selection and minimizing unnecessary detours. GA optimization is used to refine these routes by evaluating key factors, including travel distance, queue dynamics, and time, to determine paths with the fewest charging stops while maintaining efficiency. By integrating these two techniques, the proposed framework achieves a balance between computational simplicity and adaptability to changing conditions. A series of experiments have demonstrated the framework’s ability to identify the shortest and least congested routes with strategically placed charging stops. The dynamic nature of the model ensures adaptability to evolving real-world scenarios, such as fluctuating queue lengths and travel demands. This research demonstrates the effectiveness of this approach for identifying the shortest, least congested routes with the most optimal charging stations, resulting in significant advancements in sustainable transportation and EV route optimization.