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

Energy storage batteries are essential for stabilizing renewable energy systems and ensuring power grid efficiency. However, challenges such as capacity degradation, inadequate data quality, and the need for real-time predictions highlight the importance of accurate State of Health (SOH) models. This study explores Random Forest(RF), Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) for SOH prediction across single lithium-ion battery, batterywith environmental factors and battery modules. Bi-LSTM demonstrated superior performance in evaluation indicators, achieving lower MAE, MSE and R² value closest to 1, with further accuracy gains through additional features like discharge time and median voltage. The results underscore Bi-LSTM’s effectiveness in capturing long-term dependencies and its potential to enhance battery health management, contributing to reliable and sustainable energy storage systems.