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An Improved Hybrid Machine Learning Algorithm for State-of-Charge Estimation and Anomaly Detection in Smart Grid Energy Storage Systems
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DOI：10.1002/est2.70423.png)
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Abstract
En 中文
This work proposes a hybrid Piecewise AutoRegressive with eXogenous inputs-Support Vector Machine (PWARX–SVM) model for accurate State of Charge (SoC) estimation and anomaly detection in Lithium-ion Batterie Energy Storage System (Li-BESS). The model identifies discrete operational states rest, charge, and discharge while providing interpretable insights into battery behavior. Validation against experimental data shows high agreement, with R2 = 0.935 for charging and R2 = 0.959 for discharging, confirming the model's robustness and its ability to detect early anomalies such as cell degradation or connection faults. A Long Short-Term Memory (LSTM) network was also implemented for comparison, achieving higher accuracy (R2 = 0.998 and R2 = 0.992) but offering limited interpretability. While LSTM excels in prediction, the PWARX-SVM model provides a clearer understanding of physical dynamics and operational modes, essential for predictive maintenance. Overall, the proposed hybrid framework effectively bridges data-driven forecasting and physical interpretability, offering a reliable and explainable solution for real-time SoC monitoring and fault prevention in smart grid energy storage systems.
Keywords:
batteries
battery anomaly detection
identification
LSTM
prediction
PWARX-SVM
smart grid
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