An improved bidirectional long short-term memory hybrid neural network with Gaussian filtering for multi-temperature state of charge estimation of lithium-ion batteries

The new energy revolution is fundamentally reshaping the global energy structure. Power lithium batteries face issues such as charge–discharge imbalance and limited endurance. To enhance the performance and economic efficiency of power lithium batteries, an improved bidirectional long short-term memory neural network (BiLSTM) with Gaussian filtering for multi-temperature state of charge (SOC) estimation of lithium-ion batteries is proposed, named TCN-BiLSTM-SA. The algorithm employs Gaussian filtering to smooth the data, which is combined with the original data as input for data augmentation. Subsequently, the data is trained in a hybrid neural network composed of temporal convolutional networks (TCN) and BiLSTM. A self-attention mechanism (SA) is incorporated to adjust feature weights, enabling accurate prediction of the SOC for lithium-ion batteries. The proposed method was validated under various temperatures and operating conditions. The algorithm achieved a root mean square error (RMSE) of less than 1.496%, a mean absolute error (MAE) of less than 1.412%, and an R2 coefficient of determination of no less than 99.6%. These results indicate that the proposed approach exhibits high estimation accuracy and superior predictive performance.