Digital Twin-AI Framework for a Battery Management System

Battery management systems are essential to estimate internal battery states and regulate current safely under nonlinear dynamics, noisy sensing, and changing operating regimes. We propose a closed-loop digital-twin-AI framework that couples (i) a physics-informed neural network (PINN) observer for real-time state estimation, (ii) a high-fidelity single-particle-model digital twin, and (iii) a reinforcement learning (RL) controller that optimizes discharge current under different Crates. The digital twin provides physically consistent trajectories and synthetic supervision, while the PINN provides a low-latency state of charge (SOC) and state of health (SOH), from noisy measurements, allowing the RL agent to act with realistic partial observability. We evaluated the framework at different C rates, for both a single cell and a pack of cells. In addition, we include a particle swarm optimization capacity-identification module as an independent SOH benchmark. The results demonstrate stable AI-driven SOC regulation across Crates and scalable extension from cell-to pack-level monitoring. Furthermore, this approach demonstrates a clear pathway to enhance capacity-based SOH estimation accuracy through richer physics integration and expanded training coverage.