Predicting the Remaining Useful Life of Lithium-Ion Batteries Using 10 Random Data Points and a Flexible Parallel Neural Network

Predicting the Remaining Useful Life of Lithium-Ion Batteries Using 10 Random Data Points and a Flexible Parallel Neural Network

Accurate Remaining Useful Life (RUL) prediction of lithium batteries is crucial for enhancing their performance and extending their lifespan. Existing studies focus on continuous or relatively sparse datasets; however, continuous and complete datasets are rarely available in practical applications d...

Full description

Bibliographic Details
Main Authors: Lidang Jiang, Qingsong Huang, Ge He
Format: Article
Language:English
Published: MDPI AG 2024-04-01
Series:Energies
Subjects:
neural networks
lithium batteries
remaining useful life
machine learning
Online Access:https://www.mdpi.com/1996-1073/17/7/1695

Internet

https://www.mdpi.com/1996-1073/17/7/1695

Similar Items