Machine learning in energy storage material discovery and performance prediction
Energy storage material is one of the critical materials in modern life. However, due to the
difficulty of material development, the existing mainstream batteries still use the materials …
difficulty of material development, the existing mainstream batteries still use the materials …
Time‐Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction
DE Galvez‐Aranda, TL Dinh, U Vijay… - Advanced Energy …, 2024 - Wiley Online Library
The manufacturing process of Lithium‐ion battery electrodes directly affects the practical
properties of the cells, such as their performance, durability, and safety. While computational …
properties of the cells, such as their performance, durability, and safety. While computational …
Multiscale modeling for enhanced battery health analysis: Pathways to longevity
K Yang, L Zhang, W Wang, C Long, S Yang… - Carbon …, 2024 - Wiley Online Library
The issues of health assessment and lifespan prediction have always been prominent
challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews …
challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews …
Improved Rate Capability for Dry Thick Electrodes through Finite Elements Method and Machine Learning Coupling
A coupled finite elements method (FEM) and machine learning (ML) workflow is presented
to optimize the rate capability of thick positive electrodes (ca. 150 μm and 8 mAh/cm2). An …
to optimize the rate capability of thick positive electrodes (ca. 150 μm and 8 mAh/cm2). An …
Journey over destination: dynamic sensor placement enhances generalization
Reconstructing complex, high-dimensional global fields from limited data points is a
challenge across various scientific and industrial domains. This is particularly important for …
challenge across various scientific and industrial domains. This is particularly important for …
Learning a general model of single phase flow in complex 3D porous media
Modeling effective transport properties of 3D porous media, such as permeability, at multiple
scales is challenging as a result of the combined complexity of the pore structures and fluid …
scales is challenging as a result of the combined complexity of the pore structures and fluid …
Modeling of Li-Ion Battery Electrodes Accounting for Microstructure Properties: The Newman's Model Revisited
G Lenne, E Woillez, M Chandesris - Journal of The …, 2024 - iopscience.iop.org
The most established lithium-ion battery (LIB) porous-based model is the Newman's pseudo-
two-dimensional (P2D) model used as a good trade-off between numerical computational …
two-dimensional (P2D) model used as a good trade-off between numerical computational …
[PDF][PDF] Learning a general model of single phase flow in complex 3D porous media
Modeling effective transport properties of 3D porous media, such as permeability, at multiple
scales is challenging as a result of the combined complexity of the pore structures and fluid …
scales is challenging as a result of the combined complexity of the pore structures and fluid …