Degradation-Aware Operation, Diagnostics, and Digital Twins for Energy Storage Systems

Energy storage systems (ESSs) are becoming indispensable in modern power and energy infrastructures, supporting renewable energy integration, transport electrification, and resilient micro-grid operation. Among the available technologies, different types of ESSs like batteries and fuel cells have emerged as the dominant choice due to their high energy density, efficiency, and fast response. However, the long-term performance, safety, and economic viability of ESSs are strongly affected by degradation, which is driven by complex electrochemical, thermal, mechanical, and operational stress factors. Capacity fade, internal resistance growth, thermal instability, and uneven aging across cells or stacks can significantly reduce system reliability and increase life-cycle cost. Traditional ESS operation strategies often prioritize short-term performance, such as power tracking, or fast frequency response, without fully accounting for degradation dynamics. As a result, aggressive cycling, poor thermal control, and non-optimized charging/discharging profiles may accelerate aging and shorten their life. This challenge is particularly critical as ESS implementations continue to scale from consumer devices to electric vehicles, and hybrid renewable power systems. Therefore, a paradigm shift is needed from performance-oriented operation toward degradation-aware operation, where control, scheduling, and energy management decisions explicitly consider health evolution and remaining useful life. At the same time, advances in sensing, data analytics, and artificial intelligence are enabling more sophisticated diagnostics and prognostics for ESSs. Accurate estimation of state of charge (SOC), state of health (SOH), internal faults, and aging estimation are essential for safe and efficient operation. However, diagnostics remain challenging due to nonlinear dynamics, parameter uncertainty, limited measurements, and the strong coupling between operational conditions and degradation mechanisms. Emerging approaches combining physics-based models, equivalent circuit models, data-driven methods, and hybrid learning methods show strong promise for improving fault detection, health estimation, and predictive maintenance.In this context, digital twin technology has gained significant attention as an important tool for next-generation ESS management. A digital twin provides a dynamic virtual replica of the physical storage system by integrating real-time data, multi-domain models, and intelligent analytics. Such a framework can enable condition monitoring, online diagnostics, degradation prediction, and operational optimization under challenging practical conditions. For ESS applications, digital twins offer the potential to bridge the gap between laboratory-scale understanding of degradation and practical field-level decision-making.To address this, the topic seeks contributions that advance degradation-aware operation, health-conscious control, and intelligent diagnostic strategies for ESSs. Particular emphasis is placed on recent advances in physics-based modeling, data-driven health estimation, fault diagnosis, prognostics, and digital twin technologies. These approaches can enable more accurate prediction of degradation, improved remaining useful life estimation, real-time condition monitoring, and operational decision-making that balances performance with longevity. By bringing together developments in modeling, control, sensing, and AI-enabled analytics, this Research Topic aims to promote next-generation solutions that enhance the durability, safety, and economic performance of energy storage systems across both stationary and mobile applications.Ensuring the safe, reliable, and cost-effective operation of energy storage systems throughout their service life remains a major challenge, particularly under dynamic, harsh operating conditions, and large-scale deployment. Addressing this challenge requires improvements in degradation-aware operation, intelligent diagnostics, and digital twin frameworks that can capture aging behavior and support health-conscious decision-making.This Research Topic invites high-quality submissions with significant scientific and technical contributions related to the modeling, monitoring, control, and life-cycle management of energy storage systems. Topics of interest include but are not limited to the following:- Degradation modeling and aging mechanisms in energy storage systems (ESSs);- State of health, remaining useful life, and fault diagnosis of ESSs;- Degradation-aware control, operation, and energy management of ESSs;- Digital twins for condition monitoring and predictive maintenance of ESSs;- Physics-based, data-driven, and hybrid modeling approaches of ESSs;- AI and machine learning for diagnostics and prognostics of ESSs.Original research articles, review papers, and survey papers are welcome. Topic editors: Sheikh Tanzim Meraj, Avijit Das, KAMRUL HASAN Source: https://www.frontiersin.org/research-topics/81313/degradation-aware-operation-diagnostics-and-digital-twins-for-energy-storage-systems