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<p>Energy storage batteries are essential for stabilizing renewable energy systems and improving power grid efficiency. However, challenges such as capacity degradation, limited data quality, and the need for real-time evaluation highlight the importance of accurate State of Health (SOH) prediction. This study evaluates the effectiveness of Random Forest, Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) models in predicting SOH across single lithium-ion cells, cells under environmental influence, and battery modules. The Bi-LSTM model achieved a Mean Absolute Error of 0.00668, a Mean Squared Error of 0.0042, and an R<sup>2</sup> value of 0.9253 in single-cell prediction. In comparison, the Random Forest model recorded a Mean Absolute Error of 0.0523, a Mean Squared Error of 0.0159 and an R<sup>2</sup> of 0.8960, indicating a reduction in error of over 69% and a significant improvement in predictive accuracy. Incorporating physically meaningful features such as discharge time and plateau voltage further enhanced model performance. These results demonstrate the Bi-LSTM model’s strong ability to capture long-term temporal dependencies and its potential for improving intelligent battery health monitoring in real-world energy storage systems.</p>

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Machine learning-driven time series analysis for SOH prediction of lithium-ion batteries

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Yunlong Zhang,
  • Xiaolei Bi,
  • Shiqiang Wang,
  • Bin Tao

摘要

Energy storage batteries are essential for stabilizing renewable energy systems and improving power grid efficiency. However, challenges such as capacity degradation, limited data quality, and the need for real-time evaluation highlight the importance of accurate State of Health (SOH) prediction. This study evaluates the effectiveness of Random Forest, Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) models in predicting SOH across single lithium-ion cells, cells under environmental influence, and battery modules. The Bi-LSTM model achieved a Mean Absolute Error of 0.00668, a Mean Squared Error of 0.0042, and an R2 value of 0.9253 in single-cell prediction. In comparison, the Random Forest model recorded a Mean Absolute Error of 0.0523, a Mean Squared Error of 0.0159 and an R2 of 0.8960, indicating a reduction in error of over 69% and a significant improvement in predictive accuracy. Incorporating physically meaningful features such as discharge time and plateau voltage further enhanced model performance. These results demonstrate the Bi-LSTM model’s strong ability to capture long-term temporal dependencies and its potential for improving intelligent battery health monitoring in real-world energy storage systems.