A Dual-Head Mixer-BiLSTM Architecture for Battery State of Charge Prediction

State of charge (SOC) estimation is a key research topic for electric vehicles, with accurate SOC estimation being important for both range and safety. In this study, we present the Dual-Head Depth Directional Mixer (DH-DW-M) model for SOC estimation. The model is tested using the BMW i3 dataset and its performance is evaluated using standard error measures from multiple perspectives. Furthermore, the results are compared with those of previous studies; specifically, DH-DW-M is compared with the Trend Flow-Mixer model, which has achieved the best results on this dataset in the literature to date. Notably, the proposed DH-DW-M model achieves the lowest overall estimation error value of 0.21%. Compared with the Trend Flow-Mixer model, DH-DW-M showed an 82% lower Root Mean Square Error (RMSE) when using the same input features. The model is also compared with well-known methods, with RMSE approximately 97%, 96%, and 95% lower when compared to those of Long Short-Term Memory (LSTM), Convolutional Neural Network–LSTM (CNN-LSTM), and Bidirectional LSTM with Attention (BiLSTM-AT) models, respectively.