A Framework for Anomaly Cell Detection in Energy Storage Systems Based on Daily Operating Voltage and Capacity Increment Curves

This paper proposes a novel unsupervised multi-model fusion framework for robust cell-level anomaly detection in grid-scale battery energy storage systems (BESSs). Addressing the complex nonlinearity and prevalent data quality issues (e.g., asynchronous sensors, sampling anomalies) in historical operational data, the framework synergistically integrates three complementary techniques: isolation forests for efficient feature screening and dimensionality reduction; LSTM autoencoders to capture the long-term temporal dependencies in normal behavior; and a functional principal component analysis–Mahalanobis distance (FPCA-MD) for statistically rigorous anomaly validation. The fully automated workflow pioneers the combined application of feature screening, temporal modeling, and functional data validation for cell-level diagnostics. Key contributions include (1) maintaining a high detection accuracy despite asynchronous or faulty sensor data; (2) leveraging multi-dimensional operational features beyond traditional voltage curves, optimizing the utilization of historical data through tight integration of the battery characteristics with anomaly signatures; and (3) achieving enhanced performance and robustness via the complementary fusion of diverse algorithms. Comprehensive experimental results demonstrate the framework’s effectiveness in accurately identifying cells exhibiting various anomaly patterns (e.g., noise interference, performance degradation, cluster outliers) while significantly reducing the leakage and misdetection rates inherent in single-algorithm approaches, as validated by the probability scores from the fusion output.