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 …

Deep learning for prognostics and health management: State of the art, challenges, and opportunities

B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …

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 …

A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

D Yang, X Zhang, R Pan, Y Wang, Z Chen - Journal of Power Sources, 2018 - Elsevier
The state-of-health (SOH) estimation is always a crucial issue for lithium-ion batteries. In
order to provide an accurate and reliable SOH estimation, a novel Gaussian process …

A novel remaining useful life prediction method for lithium-ion battery based on long short-term memory network optimized by improved sparrow search algorithm

Y Liu, J Sun, Y Shang, X Zhang, S Ren… - Journal of Energy Storage, 2023 - Elsevier
The remaining useful life (RUL) estimation is one of the key functions of lithium-ion battery
management systems (BMS). After the battery reaches its end-of-life (EOL), its capacity …

[HTML][HTML] Gaussian process regression for forecasting battery state of health

RR Richardson, MA Osborne, DA Howey - Journal of Power Sources, 2017 - Elsevier
Accurately predicting the future capacity and remaining useful life of batteries is necessary to
ensure reliable system operation and to minimise maintenance costs. The complex nature of …

A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm

P Khumprom, N Yodo - Energies, 2019 - mdpi.com
Prognostic and health management (PHM) can ensure that a lithium-ion battery is working
safely and reliably. The main approach of PHM evaluation of the battery is to determine the …

Artificial intelligence in prognostics and health management of engineering systems

S Ochella, M Shafiee, F Dinmohammadi - Engineering Applications of …, 2022 - Elsevier
Prognostics and health management (PHM) has become a crucial aspect of the
management of engineering systems and structures, where sensor hardware and decision …

Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles

A Farmann, W Waag, A Marongiu, DU Sauer - Journal of Power Sources, 2015 - Elsevier
This work provides an overview of available methods and algorithms for on-board capacity
estimation of lithium-ion batteries. An accurate state estimation for battery management …

Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction

L Liao, F Köttig - IEEE Transactions on Reliability, 2014 - ieeexplore.ieee.org
Prognostics focuses on predicting the future performance of a system, specifically the time at
which the system no long performs its desired functionality, its time to failure. As an important …