This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

The expansion of lithium-ion batteries from consumer electronics to larger-scale transport
and energy storage applications has made understanding the many mechanisms …

Abstract Lithium-ion batteries (LIBs), as the first choice for green batteries, have been widely
used in energy storage, electric vehicles, 3C devices, and other related fields, and will have …

Zero-carbon energy and negative emission technologies are crucial for achieving a carbon
neutral future, and nanomaterials have played critical roles in advancing such technologies …

Predicting, monitoring, and optimizing the performance and health of a battery system
entails a variety of complex variables as well as unpredictability in given conditions. Data …

Digitalization of lithium-ion batteries can significantly advance the performance improvement
of lithium-ion batteries by enabling smarter controlling strategies during operation and …

An intelligent battery management system is a crucial enabler for energy storage systems
with high power output, increased safety and long lifetimes. With recent developments in …

With its extremely strong capability of data analysis, machine learning has shown versatile
potential in the revolution of the materials research paradigm. Here, taking dielectric …

Diagnostics of battery health, which encompass evaluation metrics such as state of health,
remaining useful lifetime, and end of life, are critical across various applications, from …