A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries
In the field of energy storage, it is very important to predict the state of charge and the state of
health of lithium-ion batteries. In this paper, we review the current widely used equivalent …
health of lithium-ion batteries. In this paper, we review the current widely used equivalent …
[HTML][HTML] A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries
As widely used for secondary energy storage, lithium-ion batteries have become the core
component of the power supply system and accurate remaining useful life prediction is the …
component of the power supply system and accurate remaining useful life prediction is the …
Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries
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 …
remaining useful life (RUL) prediction serves as one of the fundamental criteria for the …
Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning
Real-time and personalized lithium-ion battery health management is conducive to safety
improvement for end-users. However, personalized prognostic of the battery health status is …
improvement for end-users. However, personalized prognostic of the battery health status is …
Bayesian transfer learning with active querying for intelligent cross-machine fault prognosis under limited data
Most existing deep learning (DL)-based health prognostic methods assume that the training
and testing datasets are from identical machines operating under similar conditions …
and testing datasets are from identical machines operating under similar conditions …
A comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management
Battery ensures power solutions for many necessary portable devices such as electric
vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted …
vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted …
End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation
Real-time capacity estimation of lithium-ion batteries is crucial but challenging in battery
management systems (BMSs). Due to the complexity of battery degradation mechanism …
management systems (BMSs). Due to the complexity of battery degradation mechanism …
A hybrid battery equivalent circuit model, deep learning, and transfer learning for battery state monitoring
The accurate estimation of state of health (SOH) for lithium-ion batteries is significant to
improve the reliability and safety of batteries in operation. However, many existing studies …
improve the reliability and safety of batteries in operation. However, many existing studies …
[HTML][HTML] A long short-term memory neural network based Wiener process model for remaining useful life prediction
X Chen, Z Liu - Reliability Engineering & System Safety, 2022 - Elsevier
An unsuitable type of degradation trend function in the Wiener process-based degradation
model will negatively influence its performance when calculating remaining useful life (RUL) …
model will negatively influence its performance when calculating remaining useful life (RUL) …
A critical review of online battery remaining useful lifetime prediction methods
Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining
service life of lithium-ion batteries has become an important issue. This article reviews the …
service life of lithium-ion batteries has become an important issue. This article reviews the …