... (RMSE) are as low as 0.06% and 0.23%, respectively. Email: auyjz@hpu.edu.cn 1 Corresponding author. Email: lychhpu@163.com 07 05 2024 05 08 2024 07 08 2024 11 09 2024 Graphical Abstract Figure analysis and design of components deep learning MFCC...

... 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...

... the performance of the proposed CNN-VIT model, two classical deep learning models (CNN and CNN-LSTM) and three traditional machine learning models (GPR, SVR, and Random Forest) were selected as baseline models. Four traditional regression performance metrics were used to evaluate the results: RMSE, MAE, MAPE...

... 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...