Abstract Remaining Useful Life (RUL) prediction is a key task of Condition-based
Maintenance (CBM). The massive data collected from multiple sensors enables monitoring …

For the safe and reliable operation of battery-driven machines, accurate state-of-charge
(SOC) estimations are necessary. Unfortunately, existing methods often fail to identify …

Prognostics and health management (PHM) are gradually being applied to production
management processes as industrial production is gradually undergoing a transformation …

A latent function decomposition method is proposed for forecasting the capacity of lithium-
ion battery cells. The method uses the multioutput convolved Gaussian process (MCGP), a …

Managing the capacity of lithium-ion batteries (LiBs) accurately, particularly in large-scale
applications, enhances the cost-effectiveness of energy storage systems. Less frequent …

Although several state-of-charge (SOC) estimation methods have been proposed at the
battery cell level, limited work has been done to identify the effect of cell aging on SOC …

This paper proposes a new adaptive learning model for capacity estimation of lithium-ion
battery cells. The proposed deep neural network transfers knowledge from other cells and …

This paper proposes a new long short-term memory neural network model to estimate the
state-of-charge (SOC) of lithium-ion (Li-ion) battery cells. The proposed model improves the …

This paper proposes a neural network model for state-of-charge (SOC) estimation in lithium-
ion battery cells. The proposed deep neural network model is a cycle-based recurrent model …

With the global shift towards an ecologically conscious environment and the increasing
prominence of electric vehicles, the focus on Lithium-ion (Li-ion) batteries continues to grow …