Machine learning for a sustainable energy future
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 …
demands advances—at the materials, devices and systems levels—for the efficient …
[HTML][HTML] Overview of batteries and battery management for electric vehicles
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 …
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
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 …
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
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 …
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
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 …
the performance and cost are still not satisfactory in terms of energy density, power density …
Dynamics of particle network in composite battery cathodes
Improving composite battery electrodes requires a delicate control of active materials and
electrode formulation. The electrochemically active particles fulfill their role as energy …
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
Lithium-ion batteries are used in a wide range of applications including energy storage
systems, electric transportations, and portable electronic devices. Accurately obtaining the …
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
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 …
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
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 …
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
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 …
chemical processes in lithium (Li)‐ion batteries (LiBs). The key parameters include state‐of …