Predictive Analysis of Remaining Useful Life (RUL) of Batteries: Review

The increasing reliance on batteries for electric vehicles, renewable energy systems, and consumer electronics has necessitated advancements in predictive models for their Remaining Useful Life (RUL). This paper reviews current methodologies for RUL prediction, focusing on machine learning techniques, data-driven approaches, and hybrid models that integrate physical laws. By addressing gaps in existing research and leveraging datasets like the NASA Aging Dataset, this study identifies innovative pathways for enhancing battery health monitoring and predictive accuracy. The findings contribute to improving battery management systems, reducing costs, and fostering sustainability. Keywords— Battery Remaining Useful Life (RUL), Machine Learning, Deep Learning, Physics-Informed Models, Lithium-Ion Batteries, NASA Aging Dataset, Predictive Analytics, Real-Time Monitoring, Internet of Things (IoT), Data-Driven Models.