Temporal Fusion Transformer -LSTM-based Improved State of Charge Estimation of Li-ion battery for Real Drive Cycle

Precise measurement of State of Charge (SoC) in Liion battery is crucial for effective utilization of energy storage system. However, it is challenging with physics-based model due to computation complexity while data-driven model often lacks generalization for unseen data. To improve the robustness of data-driven model, hybrid algorithms are employed. This paper focuses on combining Temporal Fusion Transformer (TFT)Long Short-Term Memory (LSTM) to improve the generalization of state-of charge estimation of unseen drive cycles. By leveraging the attention mechanism feature, temporal feature extraction of TFT along with memory capability of LSTM TFT-LSTM model is developed with optimal hyperparameters using Optuna framework. This model is used to estimate the SoC during the discharging cycle of a Turnigy Graphene Li-Ion Battery ($5000 \mathrm{mAh}, \mathbf{6 5 C}$) for the LA92 drive cycle at an ambient temperature of $40^{\circ} \mathrm{C}$. Additionally, uncertainty analysis is performed against US06 drive cycle. Finally, the performance of TFT-LSTM is compared with LSTM model. The results demonstrate that TFT-LSTM achieves better performance compared to LSTM with Root Mean Square Error (RMSE) of $\mathbf{1. 1 9 6 2}$ for unseen US06 drive cycle. These findings demonstrate the robustness of hybrid TFTLSTM against unseen drive cycle and highlight its potential under realistic conditions with variability.