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.