Machine learning for a sustainable energy future

Z Yao, Y Lum, A Johnston, LM Mejia-Mendoza… - Nature Reviews …, 2023 - nature.com
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

[HTML][HTML] Overview of batteries and battery management for electric vehicles

W Liu, T Placke, KT Chau - Energy Reports, 2022 - Elsevier
Popularization of electric vehicles (EVs) is an effective solution to promote carbon neutrality,
thus combating the climate crisis. Advances in EV batteries and battery management …

Deep learning framework for lithium-ion battery state of charge estimation: Recent advances and future perspectives

J Tian, C Chen, W Shen, F Sun, R **ong - Energy Storage Materials, 2023 - Elsevier
Accurate state of charge (SOC) constitutes the basis for reliable operations of lithium-ion
batteries. The deep learning technique, a game changer in many fields, has recently …

Flexible battery state of health and state of charge estimation using partial charging data and deep learning

J Tian, R **ong, W Shen, J Lu, F Sun - Energy Storage Materials, 2022 - Elsevier
Accurately monitoring battery states over battery life plays a central role in building
intelligent battery management systems. This study proposes a flexible method using only …

Machine learning: an advanced platform for materials development and state prediction in lithium‐ion batteries

C Lv, X Zhou, L Zhong, C Yan, M Srinivasan… - Advanced …, 2022 - Wiley Online Library
Lithium‐ion batteries (LIBs) are vital energy‐storage devices in modern society. However,
the performance and cost are still not satisfactory in terms of energy density, power density …

Dynamics of particle network in composite battery cathodes

J Li, N Sharma, Z Jiang, Y Yang, F Monaco, Z Xu… - Science, 2022 - science.org
Improving composite battery electrodes requires a delicate control of active materials and
electrode formulation. The electrochemically active particles fulfill their role as energy …

[HTML][HTML] A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery

X Sui, S He, SB Vilsen, J Meng, R Teodorescu, DI Stroe - Applied Energy, 2021 - Elsevier
Lithium-ion batteries are used in a wide range of applications including energy storage
systems, electric transportations, and portable electronic devices. Accurately obtaining the …

A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

K Luo, X Chen, H Zheng, Z Shi - Journal of Energy Chemistry, 2022 - Elsevier
In the field of energy storage, it is very important to predict the state of charge and the state of
health of lithium-ion batteries. In this paper, we review the current widely used equivalent …

[HTML][HTML] A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries

S Wang, S **, D Bai, Y Fan, H Shi, C Fernandez - Energy Reports, 2021 - Elsevier
As widely used for secondary energy storage, lithium-ion batteries have become the core
component of the power supply system and accurate remaining useful life prediction is the …

Electrochemical impedance spectroscopy for all‐solid‐state batteries: theory, methods and future outlook

P Vadhva, J Hu, MJ Johnson, R Stocker… - …, 2021 - Wiley Online Library
Electrochemical impedance spectroscopy (EIS) is widely used to probe the physical and
chemical processes in lithium (Li)‐ion batteries (LiBs). The key parameters include state‐of …