[HTML][HTML] A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery

X Sui, S He, SB Vilsen, J Meng, R Teodorescu, DI Stroe - Applied Energy, 2021‏ - Elsevier
Lithium-ion batteries are used in a wide range of applications including energy storage
systems, electric transportations, and portable electronic devices. Accurately obtaining the …

Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling

H Rauf, M Khalid, N Arshad - Renewable and Sustainable Energy Reviews, 2022‏ - Elsevier
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 …

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Y Guo, D Yang, Y Zhang, L Wang… - Protection and Control of …, 2022‏ - ieeexplore.ieee.org
The estimation of state of health (SOH) of a lithium-ion battery (LIB) is of great significance to
system safety and economic development. This paper proposes a SOH estimation method …

Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review

A Manoharan, KM Begam, VR Aparow… - Journal of Energy …, 2022‏ - Elsevier
Abstract In recent years, Artificial Intelligence has been widely used for determining the
current state of Li-ion batteries used for Electric Vehicle applications. It is crucial to have an …

A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries

MF Ge, Y Liu, X Jiang, J Liu - Measurement, 2021‏ - Elsevier
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 …

A review of the state of health for lithium-ion batteries: Research status and suggestions

H Tian, P Qin, K Li, Z Zhao - Journal of Cleaner Production, 2020‏ - Elsevier
Lithium-ion batteries (LIBs) have become the mainstream power source for battery electric
vehicles (BEVs) with relatively superior performance. However, LIBs experience battery …

Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network

G Cheng, X Wang, Y He - Energy, 2021‏ - Elsevier
Accurate estimation and prediction of the state of health (SOH) and remaining useful life
(RUL) are crucial for battery management systems, which have an important role in the field …

A comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management

SA Hasib, S Islam, RK Chakrabortty, MJ Ryan… - Ieee …, 2021‏ - ieeexplore.ieee.org
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 …

An overview of data-driven battery health estimation technology for battery management system

M Chen, G Ma, W Liu, N Zeng, X Luo - Neurocomputing, 2023‏ - Elsevier
Battery degradation, caused by multiple coupled degradation mechanisms, severely affects
the safety and sustainability of a battery management system (BMS). The battery state of …

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

X Shu, S Shen, J Shen, Y Zhang, G Li, Z Chen, Y Liu - Iscience, 2021‏ - cell.com
Accurate state of health (SOH) prediction is significant to guarantee operation safety and
avoid latent failures of lithium-ion batteries. With the development of communication and …