RESEARCH PAPER
Application of DBN-based KRLS method for RUL prediction of lithium-ion batteries
1
Lanzhou Jiaotong University, Lanzhou, Gansu 730070, P.R. China, China
Submission date: 2024-07-19
Final revision date: 2024-09-04
Acceptance date: 2024-10-06
Online publication date: 2024-10-09
Publication date: 2024-10-09
Corresponding author
Jun Li
Lanzhou Jiaotong University, Lanzhou, Gansu 730070, P.R. China, mailbox 602, Lanzhou JiaoTong University,P.R.China, 730070, lanzhou, China
Lanzhou Jiaotong University, Lanzhou, Gansu 730070, P.R. China, mailbox 602, Lanzhou JiaoTong University,P.R.China, 730070, lanzhou, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):194174
HIGHLIGHTS
- Using DBN for lithium battery feature extraction solves the problem of difficulty in ex-tracting features from lithium batteries.
- By adopting the concept of dual alternate learning, an SCKF-FB-KRLS fusion model is proposed for RUL prediction of the battery.
- By combining the historical capacity data of lithium batteries, results can be obtained quickly and accurately.
KEYWORDS
TOPICS
ABSTRACT
The Remaining Useful Life (RUL) of lithium batteries is vital for maintaining and safely operating the batteries, making precise RUL predictions highly significant. This paper introduces a method for predicting the RUL of lithium-ion batteries, utilizing a kernel adaptive filtering algorithm integrated with Deep Belief Networks (DBN). The method constructs a novel prediction model based on the Fixed-Budget Kernel Recursive Least Squares (FB-KRLS) algorithm. In this approach, the DBN extracts features from the original lithium battery data to reduce data complexity. The Square-root Cubature Kalman Filter (SCKF) is integrated with the FB-KRLS algorithm, employing a dual al-ternating learning strategy to improve the model's nonlinear fitting performance. The model was validated using NASA's lithium battery data, showing that the minimum val-ues for the MAPE, RMSE and MAE were 0.102%, 0.0016 and 0.0014, respectively. Therefore, the proposed method demonstrates potential for application in predicting the RUL of lithium-ion batteries.
ACKNOWLEDGEMENTS
This work was financially supported by the National Natural Science Foundation of China (51467008), Gansu Provincial Department of Education Industry Support Program (2021CYZC-32), Gansu Provincial Science and Technology Program (23JRRA892, 24JRRA243).
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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