Skip to main content

Research Repository

Advanced Search

An improved rainflow algorithm combined with linear criterion for the accurate li-ion battery residual life prediction.

Huang, Junhan; Wang, Shunli; Xu, Wenhua; Fernandez, Carlos; Fan, Yongcun; Chen, Xianpei

Authors

Junhan Huang

Shunli Wang

Wenhua Xu

Yongcun Fan

Xianpei Chen



Abstract

Li-ion battery health assessment has been widely used in electric vehicles, unmanned aerial vehicle and other fields. In this paper, a new linear prediction method is proposed. By weakening the sensitivity of the Rainflow algorithm to the peak data, it can be applied to the field of battery, and can accurately count the number of Li-ion battery cycles, and skip the cumbersome link of parameter identification. Then, a linear criterion is proposed based on the idea of proportion, which makes the life prediction of Li-ion battery linear. Under the verification of multiple sets of data, the prediction error of this method is kept within 2.53%. This method has the advantages of high operation efficiency and simple operation, which provides a new idea for battery life prediction in the field of electric vehicles and aerospace.

Citation

HUANG, J., WANG, S., XU, W., FERNANDEZ, C., FAN, Y. and CHEN, X. 2021. An improved rainflow algorithm combined with linear criterion for the accurate li-ion battery residual life prediction. International journal of electrochemical science [online], 16(7), article ID 21075. Available from: https://doi.org/10.20964/2021.07.29

Journal Article Type Article
Acceptance Date Apr 3, 2021
Online Publication Date May 31, 2021
Publication Date Jul 31, 2021
Deposit Date Jul 1, 2021
Publicly Available Date Jul 1, 2021
Journal International Journal of Electrochemical Science
Print ISSN 1452-3981
Electronic ISSN 1753-1063
Publisher Electrochemical Science Group
Peer Reviewed Peer Reviewed
Volume 16
Issue 7
Pages 21075
DOI https://doi.org/10.20964/2021.07.29
Keywords Li-ion battery; State of charge; Unscented Kalman filtering; Rainflow; Linear prediction criterion
Public URL https://rgu-repository.worktribe.com/output/1375477

Files





You might also like



Downloadable Citations