Utilizing machine learning to forecast the charging patterns of electric vehicles

This project involves the application of advanced machine learning techniques to forecast the charging behaviors of electric vehicles (EVs), addressing the growing demand for a robust and efficient charging infrastructure as EV adoption accelerates. Utilizing historical data from NL Hydro’s public EV charging network, this research aims to develop predictive models that can optimize charging schedules, reduce peak demand on the power grid, and enhance overall charging efficiency. This study applies a variety of machine learning algorithms, including Isolation Forest for anomaly detection, Support Vector Regression for precise regression tasks, Random Forest for robust predictive modeling, XGBoost for high-efficiency gradient boosting, and ensemble methods such as Stacking Regressor to improve predictive accuracy by combining multiple models. These algorithms help analyze key factors such as the starting state of charge (SOC), energy consumption during charging sessions, and the duration of charging events. The models are designed to predict charging behavior patterns, providing insights into how EV users interact with charging infrastructure. The findings reveal that EV users mainly engage in short, frequent charging sessions, typically beginning when the SOC is at a medium level and concluding when it reaches a high level. This pattern suggests a strategic approach to optimizing driving range while reducing concerns about running out of battery. The project contributes to the advancement of intelligent transportation systems by offering data-driven insights that can guide policymakers, utility companies, and the car industry. By optimizing EV charging infrastructure, the study supports the broader goal of sustainable mobility, facilitating the transition to electric transportation while achieving long-term environmental and economic benefits