Discharge Voltage Prediction Model of Batteries in Different Degradation States Based on IWOA-ATCN

As a key technology for future decarbonization, storage batteries are widely used in areas such as electric vehicles and power systems. However, battery aging inevitably limits their broader application. To address the low accuracy in predicting discharge voltage under different aging states, this paper proposes the IWOA-ATCN method based on a TCN model with a sliding window mechanism. First, the improved whale optimization algorithm (IWOA) is employed to optimize the hyperparameters in the TCN model, including window size and sliding step, to obtain the optimal sample structure. Then, the temporal attention mechanism is introduced into the TCN model to accurately capture the temporal correlation of discharge voltage, thereby improving the prediction accuracy of long time series data. Finally, the model is tested on the NASA dataset with an RMSE of 0.0072, MAE of 0.0046, and R2 of 0.9984. The test results on the PL Sample dataset showed RMSE of 0.0081, MAE of 0.0040, and R2 of 0.9983. It is indicated that the prediction accuracy and stability of the IWOA-ATCN model are higher than other models, such as BP, RNN, and LSTM.