AI-Enhanced Digital Twin with Hardware-in-the-Loop Deployment for Real-Time Lithium-Ion Battery State-of-Health Estimation

This work presents an AI-enhanced battery Digital Twin combining an aging-adaptive equivalent circuit model, an SOH-coupled Unscented Kalman Filter, and a systematic seven-model comparison for SOH prediction, validated not only through leave-one-battery-out cross-validation on the NASA dataset but also through live deployment on a physical hardware-in-the-loop sensor rig with closed-loop fan actuation. Unlike most reported battery Digital Twins, which are validated purely in simulation, this system demonstrates that a physics-informed, ML-benchmarked architecture can be deployed and cross-checked on real sensor hardware in real time.