Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on Empirical Mode Decomposition and Deep Neural Networks
The prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is vital for the battery management system used in electric vehicles. We can avoid unnecessary losses if we can accurately predict the RUL of batteries and replace batteries on time. This study proposes a method for pr...
Main Authors: | Jianshu Qiao, Xiaofeng Liu, Zehua Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9019646/ |
Similar Items
-
Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Variational Mode Decomposition and Machine Learning Algorithm
by: Chuang Sun, et al.
Published: (2022-12-01) -
Remaining-Useful-Life Prediction for Li-Ion Batteries
by: Yeong-Hwa Chang, et al.
Published: (2023-03-01) -
An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD
Published: (2020-08-01) -
Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction
by: Siyu Jin, et al.
Published: (2021-12-01) -
XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries
by: Sadiqa Jafari, et al.
Published: (2022-12-01)