AI-Driven Predictive Maintenance for Energy Storage Systems: Enhancing Reliability and Lifespan

Abstract

Energy storage systems (ESS) are critical for the reliable integration of renewable energy sources and the stabilization of power grids. However, these systems face challenges related to operational efficiency, component wear, and unexpected failures, all of which can impact reliability and lifespan. AI-driven predictive maintenance offers a transformative solution by leveraging machine learning and data analytics to forecast failures, optimize maintenance schedules, and enhance overall system performance. This paper explores the integration of AI in predictive maintenance strategies for ESS, focusing on how advanced algorithms can monitor system health, predict failures before they occur, and reduce downtime. Case studies and simulations are presented to demonstrate how AI models can predict battery degradation, component failures, and performance anomalies, leading to extended system longevity and improved operational reliability. The findings indicate that AI-driven maintenance can significantly lower operational costs, reduce the risk of unexpected failures, and support the development of more resilient energy storage infrastructures.