The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …

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 …

Safety assurance is essential for lithium-ion batteries in power supply fields, and the
remaining useful life (RUL) prediction serves as one of the fundamental criteria for the …

We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

The state-of-health (SOH) estimation is a challenging task for lithium-ion battery, which
contribute significantly to maximize the performance of battery-powered systems and guide …

The use of a modern technological system requires a good engineering approach,
optimized operations, and proper maintenance in order to keep the system in an optimal …

Lithium-ion battery remaining useful life (RUL) is an essential technology for battery
management, safety assurance and predictive maintenance, which has attracted the …

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Prognostic and health management (PHM) can ensure that a lithium-ion battery is working
safely and reliably. The main approach of PHM evaluation of the battery is to determine the …