Voltage Prediction of Lithium‐Ion Battery Based on Time Contrastive Learning Model

This study presents a neural network approach to predict the voltage of lithium‐ion batteries. The model employs three voltage data enhancement techniques to improve the feature representation of voltage data. It utilizes a deep dilated convolution network combined with a self‐supervised triplet loss function based on negative sampling. This approach is designed to learn a time series representation method that effectively addresses the issue of missing data. Subsequently, the model leverages a Transformer architecture to extract high‐dimensional characteristics of voltage data. In a battery pack or battery array, monitoring multiple batteries simultaneously allows the encoder part to process the entire sequence in parallel. Finally, voltage prediction is achieved through time contrast learning. The results demonstrate that the model outperforms conventional neural network models across three voltage datasets. Notably, on the NASA dataset, the model reduces the relative error in voltage time series prediction by 2.31% compared to the Transformer model and by 16.27% compared to the long short‐term memory model. In the large‐scale voltage dataset of LiFePO 4 battery in an energy storage power station, the performance is even better, and the task of accurate and efficient prediction has been accomplished excellently.