Electric Vehicle Energy Demand Prediction Techniques: A Critical and Systematic Review

Accurately predicting energy demand is crucial for managing charging infrastructure, maximising vehicle performance, and guaranteeing effective energy distribution as EV adoption picks up speed. This study offers a thorough and organised analysis of EV energy demand prediction methods, covering deep learning frameworks, machine learning models, and conventional statistical methods. It also presents a useful implementation using a web application built with Flask that forecasts EV energy use depending on variables like speed, temperature, battery capacity, and distance travelled. In order to provide real-time, easily navigable predictions, the system combines a learnt machine learning regressor with a data scaler. The entire pipeline—data preprocessing, model training, application design, and performance evaluation—is described in this paper, providing theoretical understanding and a practical solution for EV energy demand forecasting.