Real-Time Fault Diagnosis and Reliability Analysis in Lithium-Ion BESS via Bayesian Fault Propagation Networks

The rapid integration of intermittent renewable energy sources (RES) poses significant operational challenges for modern power systems. Lithium-ion battery (LIB)–based battery energy storage systems (BESS) have become vital for grid stability and energy management. However, large-scale deployment of BESS has led to increasing incidents such as fires and explosions, raising serious concerns regarding their safety and reliability. To overcome the limitations of traditional reliability assessment methods—such as reliability block diagrams (RBD), fault tree analysis (FTA), and Markov models—this study proposes an integrated fault detection and reliability analysis framework that combines FTA, failure mode and effects analysis (FMEA), and a Bayesian Fault Propagation Network (BFPN). The framework systematically models fault propagation across component, subsystem, and system levels, dynamically updating the prior probabilities of basic failure events using a Gaussian Mixture Model (GMM) and Expectation–Maximization (EM) algorithm. Conditional Probability Tables (CPTs) are recalculated through Maximum Likelihood Estimation (MLE) with logical relationships to achieve accurate and adaptive fault probability estimation. A multi-feature fusion indicator, the State Severity Indicator (SSI), is further introduced to evaluate system health in real time. A qualitative comparison with representative fault modeling and detection approaches—including Bayesian Network, FTA-DBN, and various machine learning methods—shows that the proposed BFPN offers a well-balanced trade-off between interpretability and real-time performance. Simulation experiments under both single- and multiple-fault scenarios demonstrate that the proposed framework accurately detects typical fault events and provides early warnings before fault escalation. Under complex coupled fault conditions, it effectively captures fault interactions and predicts cascading failures across subsystems and the overall BESS, showing strong robustness and diagnostic capability for real-time reliability assessment in modern energy storage systems.