DATA DRIVEN ENERGY ECONOMY PREDICTION FOR ELECTRIC CITY BUSES USING MACHINE LEARNING

Abstract

The growing adoption of electric city buses has become an essential component of sustainable urban transportation systems aimed at reducing carbon emissions and improving energy efficiency. Effective management of electric bus operations requires accurate prediction of energy economy to optimize route planning, battery utilization, charging schedules, and operational costs. Traditional energy estimation methods often struggle to capture the complex interactions among vehicle characteristics, driving behavior, traffic conditions, weather parameters, and route profiles. This paper presents a data-driven energy economy prediction framework for electric city buses using machine learning techniques. The proposed system utilizes historical operational data, vehicle performance metrics, environmental conditions, and route information to develop predictive models capable of estimating energy consumption with high accuracy. Machine learning algorithms are employed to identify hidden relationships among multiple influencing factors and generate reliable energy economy forecasts. Experimental analysis demonstrates that the proposed approach significantly improves prediction accuracy, enhances fleet management efficiency, and supports intelligent decision-making in electric transportation systems. The developed framework provides a scalable and intelligent solution for optimizing energy utilization and promoting sustainable urban mobility.