Federated Learning-Based Energy Management for Electric Vehicles

Abstract

The swift integration of electric vehicles (EVs) necessitates the development of sophisticated energy management strategies that enhance battery efficiency, driving range, and system reliability. Conventional centralized machine learning methodologies require extensive data sharing, which raises concerns regarding privacy and communication overhead. This paper introduces a Federated Learning (FL)-based Energy Management System (EMS) for electric vehicles, facilitating collaborative model training without the need to share raw vehicle data. The proposed framework incorporates distributed onboard learning, adaptive battery optimization, and real-time decision-making. A deep neural network model is trained locally on multiple EV nodes and aggregated through a federated server utilizing secure parameter exchange. Simulation results indicate improved state-of-charge prediction accuracy, reduced energy consumption, and extended battery life compared to centralized and rule-based methods. The proposed approach ensures privacy preservation while achieving scalable and efficient energy optimization for EVs.