The remarkable success of deep learning (DL) in predicting battery health has prompted
interest in its application in recent years. While state-of-the-art DL models have achieved …

This paper presents a comprehensive survey of machine learning, deep learning, and digital
twin technology methods for predicting and managing the battery state of health in electric …

As the demand for electric vehicles (EVs) rises globally, ensuring the safety and reliability of
EV battery systems becomes paramount. Accurately predicting the state of health (SoH) and …

This paper proposes a blockchain-based method for assessment and tracking of electric
vehicle battery degradation costs. Vehicle-to-Grid (V2G) technology allows the bidirectional …

The environmental sustainability stimulates the development of electric vehicles with great
energy-saving and emission reduction effects. State of health of the battery system in an …

Establishing an accurate mathematical model is fundamental to managing, monitoring, and
protecting the battery pack in electric vehicles (EVs). The application of the deep learning …

Electric vehicles (EVs) is the most useful by-product of renewable technologies in
transportation domain due to ecofriendly and user-friendly nature. EVs that run on batteries …

An accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe
and reliable operation of electric vehicles (EVs). Feature-based machine learning methods …

Online accurate battery state-of-health (SOH) estimation is crucial for ensuring safe and
reliable operations of electric vehicles (EVs). Yet, such estimation problem remains a …

The widespread adoption of EVs is limited by their reliance on batteries with presently low
energy and power densities compared to liquid fuels and are subject to aging and …