Federated Digital Twin Reinforcement Learning Framework with Physics Informed Intelligence for Autonomous Battery Management Systems

Abstract

The rapid evolution of electric vehicles, smart grids, and energy storage infrastructures has exposed critical limitations in conventional Battery Management Systems (BMS), particularly in adaptive intelligence, privacy preservation, degradation awareness, and real-time decision autonomy. This paper proposes a novel Federated Physics-Informed Digital Twin Reinforcement Learning Battery Management System (FPD-RL BMS), an autonomous multi-layered framework that integrates Physics-Informed Neural Networks (PINNs), federated edge learning, deep reinforcement learning, and synchronized digital twin technology into a unified intelligent battery ecosystem. Unlike traditional AI-based BMS approaches that rely on centralized data training and isolated state estimation models, the proposed framework introduces a unique Adaptive Federated Physics-Informed Deep Reinforcement Learning (AFPIDRL) algorithm capable of jointly optimizing State-of-Charge (SOC), State-of-Health (SOH), thermal stability, charging efficiency, and battery lifespan under dynamic operating conditions. The methodology enables distributed vehicles or battery nodes to collaboratively learn degradation patterns without sharing raw data, thereby ensuring cybersecurity and privacy preservation. Additionally, a continuously evolving digital twin provides real-time electrochemical synchronization and predictive fault intelligence, while the reinforcement learning agent autonomously adapts charging and balancing strategies based on aging-aware reward functions. Experimental simulations demonstrate that the proposed architecture significantly enhances prediction accuracy, thermal safety, energy efficiency, and long-term battery sustainability compared with existing AI-driven BMS frameworks.