Yifei Zhou
Remaining useful life prediction hybrid model of lithium-ion battery based on improved GWO-LightGBM.
Zhou, Yifei; Wang, Shunli; Shen, Xianfeng; Sojib, Ahamed S M; Fernandez, Carlos
Authors
Shunli Wang
Xianfeng Shen
Ahamed S M Sojib
Dr Carlos Fernandez c.fernandez@rgu.ac.uk
Senior Lecturer
Abstract
Lithium-ion batteries, as the core of new energy vehicles, determine the safety of new energy vehicles. Remaining useful life of the battery is the most important parameter, and it is particularly important to estimate the remaining life accurately. This paper proposes a hybrid algorithm of GWO algorithm and LightGBM algorithm based on improved convergence factor and proportional weights, which is used to predict the remaining life of lithium-ion batteries. It is verified by using NASA data set, which proves that the optimization of GWO algorithm can significantly improve LightGBM algorithm. RMSE, MAE and MAPE increased by 71.46%,80.59% and 75.79% respectively.
Citation
ZHOU, Y., WANG, S., SHEN, X., SOJIB, A.S.M. and FERNANDEZ, C. 2024. Remaining useful life prediction hybrid model of lithium-ion battery based on improved GWO-LightGBM. In Proceedings of the 25th IEEE (Institute of Electrical and Electronics Engineers) China conference on system simulation technology and its application 2024 (CCSSTA 2024), 21-23 July 2024, Tianjin, China. Piscataway: IEEE [online], pages 553-556. Available from: https://doi.org/10.1109/CCSSTA62096.2024.10691744
| Presentation Conference Type | Conference Paper (published) |
|---|---|
| Conference Name | 25th IEEE (Institute of Electrical and Electronics Engineers) China conference on system simulation technology and its application 2024 (CCSSTA 2024) |
| Start Date | Jul 21, 2024 |
| End Date | Jul 23, 2024 |
| Acceptance Date | Jun 30, 2024 |
| Online Publication Date | Jul 23, 2024 |
| Publication Date | Dec 31, 2024 |
| Deposit Date | Oct 3, 2024 |
| Publicly Available Date | Oct 3, 2024 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Peer Reviewed | Peer Reviewed |
| Pages | 553-556 |
| DOI | https://doi.org/10.1109/ccssta62096.2024.10691744 |
| Keywords | Remaining useful life; Improved convergence factor; Proportional weights; Grey wolf optimization algorithm; LightGBM algorithm; Hybrid algorithm |
| Public URL | https://rgu-repository.worktribe.com/output/2509761 |
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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