Lithium-ion batteries play a pivotal role in a wide range of applications, from electronic
devices to large-scale electrified transportation systems and grid-scale energy storage …

Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core
of transformational developments in battery design, modelling and management is data. In …

Abstract Machine learning has emerged as a transformative force throughout the entire
engineering life cycle of electrochemical batteries. Its applications encompass a wide array …

An accurate aging forecasting and state of health estimation is essential for a safe and
economically valuable usage of lithium-ion batteries. However, the non-linear aging of …

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 …

Emerging machine learning (ML) methods are widely applied in chemistry and materials
science studies and have led to a focus on data‐driven research. This Minireview …

Accurate battery lifetime estimates enable accelerated design of novel battery materials and
determination of optimal use protocols for longevity in deployments. Unfortunately …

The recycling of spent batteries is an important concern in resource conservation and
environmental protection, while it is facing challenges such as insufficient recycling …

Lithium-ion battery (LIB) degradation is often characterized at three distinct levels:
mechanisms, modes, and metrics. Recent trends in diagnostics and prognostics have been …

A comprehensive understanding of lithium-ion battery (LiB) lifespan is the key to designing
durable batteries and optimizing use protocols. Although battery lifetime prediction methods …