EV PREDICTING LOCATION

The rapid growth of electric vehicles (EVs) has transformed the global transportation ecosystem. However, the increasing number of EVs has created challenges related to charging infrastructure, traffic congestion, and energy demand forecasting. Predicting the real-time and future location of electric vehicles using Artificial Intelligence (AI) plays a crucial role in smart transportation systems and sustainable urban planning. This research proposes an AIdriven framework for predicting EV movement patterns and determining optimal charging station placement using machine learning algorithms.

The proposed system integrates GPS trajectory data, traffic density information, battery level data, and environmental variables to forecast EV location and charging demand. Multiple models including Long Short-Term Memory (LSTM), Random Forest, and Gradient Boosting are evaluated for prediction accuracy. The study demonstrates that AI-based prediction improves charging station utilization efficiency, reduces waiting times, and supports energy grid stability.