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 …

Prognostics and health management (PHM) is developed to guarantee the safety and
reliability of Lithium-ion (Li-ion) battery during operations. Due to the advantages of deep …

In order for lithium-ion batteries to function reliably and safely, accurate capacity and
remaining useful life (RUL) predictions are essential, but challenging. Some current deep …

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 …

Accurate battery life prediction is a critical part of the business case for electric vehicles,
stationary energy storage, and nascent applications such as electric aircraft. Existing …

Designing and deployment of state-of-the-art electric vehicles (EVs) in terms of low cost and
high driving range with appropriate reliability and security are identified as the key towards …

Integration of each aspect of the manufacturing process with the new generation of
information technology such as the Internet of Things, big data, and cloud computing makes …

Abstract Approaches such as Cyber-Physical Systems (CPS), Internet of Things (IoT),
Internet of Services (IoS), and Data Analytics have built a new paradigm called Industry 4.0 …

There is an increasing demand for modern diagnostic systems for batteries under real-world
operation, specifically for the estimation of their state of health, for example, via their …

The increasing availability of sensor monitoring data has stimulated the development of
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …