MACHINE LEARNING-BASED ENERGY CONSUMPTION PREDICTION AND OPTIMIZATION FOR ELECTRIC VEHICLES

Accurate prediction and effective optimization of energy consumption are pivotal to the advancement of electric vehicle (EV) technologies. This paper presents a machine learning-based framework for modeling, predicting, and minimizing EV energy consumption under varying operational conditions. By integrating real-world driving data with advanced regression and classification models, the study achieves high-accuracy forecasts of energy usage and proposes dynamic optimization strategies for enhanced efficiency. Experimental evaluations demonstrate that the proposed methods can reduce energy waste by up to 15% compared to conventional strategies. These results underscore the potential of data-driven approaches in driving sustainable electric mobility.