Blockchain-Based Federated Learning for EV Energy Prediction

The rapid growth of Electric Vehicles (EVs) has increased the need for accurate energy consumption prediction to support efficient battery management and charging infrastructure. This project proposes a decentralized approach that combines Federated Learning and Blockchain to ensure secure and privacy-preserving model training. Each participating node trains the model locally and shares only model updates instead of raw data. Deep learning techniques such as RNN and LSTM are used to analyze driving patterns and battery usage. The integration of Blockchain ensures transparency, trust, and secure management of model updates.