Battery Digital Twin –RUL prediction using ML algorithms & implimentation on Edg

Abstract

Battery systems are crucial for powering a wide range of devices, from portable electronics to electric vehicles and renewable energy storage. Accurately estimating the Remaining Useful Life (RUL) of batteries is essential for optimizing their performance, ensuring reliable operation, and minimizing downtime. This paper explores the prediction of Battery RUL using machine-learning techniques and discusses the deployment of these models on edge devices for real-time monitoring and decision-making.