Remaining useful life prediction of Lithium-ion batteries based on PSO-RF algorithm

Wu, Jingjin and Cheng, Xukun and Huang, Heng and Fang, Chao and Zhang, Ling and Zhao, Xiaokang and Zhang, Lina and Xing, Jiejie (2023) Remaining useful life prediction of Lithium-ion batteries based on PSO-RF algorithm. Frontiers in Energy Research, 10. ISSN 2296-598X

[thumbnail of pubmed-zip/versions/2/package-entries/fenrg-10-937035-r1/fenrg-10-937035.pdf] Text
pubmed-zip/versions/2/package-entries/fenrg-10-937035-r1/fenrg-10-937035.pdf - Published Version

Download (7MB)

Abstract

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is the key to the battery health management system. However, problems of unstable model output and extensive calculation limit the prediction accuracy. This article proposes a Particle Swarm Optimization Random Forest (PSO-RF) prediction method to improve the RUL prediction accuracy. First, the battery capacity extracted from the lithium-ion battery data set of the National Aeronautics and Space Administration (NASA) and the University of Maryland Center for Advanced Life Cycle Engineering (CALCE) is set as the battery life health factor. Then, a PSO-RF prediction model is established based on the optimal parameters for the number of trees and the number of random features to split by the PSO algorithm. Finally, the experiment is verified on the NASA and CALCE data sets. The experiment results indicate that the method predicts RUL with Mean Absolute Error (MAE) less than 2%, Root Mean Square Error (RMSE) less than 3%, and goodness of fit greater than 94%. This method solves the problem of parameter selection in the RF algorithm.

Item Type: Article
Subjects: EP Archives > Energy
Depositing User: Managing Editor
Date Deposited: 04 May 2023 04:50
Last Modified: 31 Jan 2024 04:07
URI: http://research.send4journal.com/id/eprint/2026

Actions (login required)

View Item
View Item