[HTML][HTML] A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery
Lithium-ion batteries are used in a wide range of applications including energy storage
systems, electric transportations, and portable electronic devices. Accurately obtaining the …
systems, electric transportations, and portable electronic devices. Accurately obtaining the …
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 …
Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling
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 …
high driving range with appropriate reliability and security are identified as the key towards …
[HTML][HTML] Lithium-ion battery data and where to find it
Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core
of transformational developments in battery design, modelling and management is data. In …
of transformational developments in battery design, modelling and management is data. In …
A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries
Lithium-ion batteries have been generally used in industrial applications. In order to ensure
the safety of the power system and reduce the operation cost, it is particularly important to …
the safety of the power system and reduce the operation cost, it is particularly important to …
A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery
Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is
a key but challenging issue in the applications of battery health diagnosis and management …
a key but challenging issue in the applications of battery health diagnosis and management …
[HTML][HTML] Deep neural network battery charging curve prediction using 30 points collected in 10 min
Accurate degradation monitoring over battery life is indispensable for the safe and durable
operation of battery-powered applications. In this work, we extend conventional capacity …
operation of battery-powered applications. In this work, we extend conventional capacity …
Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning
Accurate degradation trajectory and future life are the key information of a new generation of
intelligent battery and electrochemical energy storage systems. It is very challenging to …
intelligent battery and electrochemical energy storage systems. It is very challenging to …
Transformer network for remaining useful life prediction of lithium-ion batteries
D Chen, W Hong, X Zhou - Ieee Access, 2022 - ieeexplore.ieee.org
Accurately predicting the Remaining Useful Life (RUL) of a Li-ion battery plays an important
role in managing the health and estimating the state of a battery. With the rapid development …
role in managing the health and estimating the state of a battery. With the rapid development …