Advanced State Estimation and Fault Diagnosis for Battery Energy Storage Systems

The field of battery energy storage systems (BESS) has become pivotal to modern energy infrastructure, catalyzing progress in transportation electrification, renewable integration, and distributed energy applications. Recent advancements in lithium-ion and emerging battery technologies have significantly enhanced energy density and efficiency, yet their safe, reliable, and long-term operation remains challenging. The complexity of BESS—arising from dynamic operating conditions, nonlinear behaviors, and strong coupling between electrochemical, thermal, and mechanical domains—underscores the urgent need for accurate state estimation and effective fault diagnosis. Ongoing debates focus on how to balance model interpretability with prediction accuracy, manage uncertainties due to aging and environmental influences, and integrate diagnostics into real-world systems without significant computational burdens. Despite advances in model-based estimation, data-driven analytics, and hybrid methodologies, current frameworks often lack robustness and scalability when deployed in evolving and diverse application settings. There is a growing demand for adaptive, lightweight, and real-time solutions capable of maintaining reliability and safety while meeting the rigor of large-scale, high-performance energy networks.This Research Topic aims to advance robust state estimation, health assessment, and fault diagnosis methodologies for battery energy storage systems operating under nonlinear, multi-physics, and uncertain conditions. It seeks to bridge theoretical modeling and practical deployment by fostering interdisciplinary contributions that enhance the understanding of internal battery states, fault propagation mechanisms, and health degradation patterns. In particular, the Topic encourages research on intelligent, scalable systems that integrate model-based and data-driven approaches for the monitoring, control, and protection of BESS in real-world contexts. The objective is to develop sustainable strategies that ensure higher resilience, safety, and operational efficiency through experimental validation, real-world field studies, and edge-computing-ready frameworks.The research will focus on advancements within battery state monitoring and diagnostics while excluding topics related purely to manufacturing, chemistry development, or non-electrochemical storage technologies. To gather further insights into advanced methodologies for state estimation and fault diagnosis, we welcome articles addressing, but not limited to, the following themes:State estimation and health assessment, including State of Charge, State of Health, State of Power, State of Temperature, and Remaining Useful LifeFault detection and diagnosis for cells, modules, battery packs, and management systems, covering fault types such as imbalances, short circuits, and thermal runawayMonitoring and diagnostics for power electronics and energy conversion interfaces, including DC-DC converters, inverters, vehicle-to-grid systems, and charging infrastructureLightweight, scalable, and real-time monitoring or diagnostic frameworks for edge and embedded computing environmentsModel-based, data-driven, and hybrid approaches for systematic modeling, predictive analytics, and state estimation under multi-domain coupling Topic editors: HUAMIN JIE, Yongqi Chang, Yuchen He Source: https://www.frontiersin.org/research-topics/79806/advanced-state-estimation-and-fault-diagnosis-for-battery-energy-storage-systems