.... In conclusion, the proposed methodology leads to high accuracy in estimating the battery SOH. The time-frequency feature fusion method based on the transformer model could provide a novel approach for battery management systems, which could prove useful for deep learning model research. Experimental...

... significant challenges. This work explores the feasibility of designing a neural network specifically for solving diffusion-induced stress in the electrode of lithium-ion battery via deep learning techniques. A loss function is constructed from the spatiotemporal coordinates of sampling points within...

...Wei Liu; Songchen Gao; Wendi Yan Rapid and accurate estimation of the state of health of lithium-ion batteries is of great significance. This paper aims to address two issues faced when applying deep learning methods to estimate the health status of lithium-ion batteries: high data quality...

.... However, using machine learning algorithms requires manual extraction of highly correlated features with the SOH, which increases model uncertainty. With the development of deep learning, automatic feature extraction has become a reality. Consequently, more and more researchers are using deep learning...

... input feature was added to the SF input feature for each atom which was its own atomic number. Thus, each atom was represented by 163 input features in our study. 2D materials interfaces deep learning tin DFT potential energy surface batteries novel materials novel numerical and analytical...