Federated Learning-Based Electric Vehicle Energy Consumption Prediction and Charging Station Recommendation

Electric vehicles (EVs) offer significant potential for reducing emissions, yet their expansion is constrained by long charging times, limited charging infrastructure, and inefficient charging station (CS) utilization. This study proposes an intelligent platform that explicitly supports drivers in multiple aspects: predicting EV energy consumption (EVEC), estimating the remaining energy at the destination, and determining the remaining driving range, thereby assisting drivers in deciding whether to continue their trip or stop for recharging. The system also recommends the most appropriate CS by considering driver preferences. Using SUMO simulations with OpenStreetMap data to prepare realistic traffic scenarios, the platform combines ensemble machine learning (ML) models with federated learning (FL) to optimize EV charging decisions. Through the integration of Bi-LSTM + XGBoost for EVEC prediction and FFNN + XGBoost for the optimal CS selection, the proposed framework significantly outperforms conventional methods by minimizing total travel time and resulting in mean absolute error (MAE) values ranging from 2.3 to 4.5 minutes under varying traffic conditions.