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).
REFERENCES (61)
1.
Alsuwian T, Ansari S, Zainuri MA, Ayob A, Hussain A, Lipu MH, Alhawari AR, Almawgani AH, Almasabi S, Hindi AT. A Review of.
2.
Expert Hybrid and Co-Estimation Techniques for SOH and RUL Estimation in Battery Management System with Electric Vehicle.
3.
Application. Expert Systems with Applications. 2024 Jan 4:123123, https://doi.org/10.1016/j.eswa....
4.
Ge MF, Liu Y, Jiang X, Liu J. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries.
6.
Liu F, Yu D, Shao C, Liu X, Su W. A review of multi-state joint estimation for lithium-ion battery: Research status and suggestions. Journal.
8.
Ning J, Xiao B, Zhong W, Xiao B. A rapid detection method for the battery state of health. Measurement. 2022 Feb 15;189:110502,.
10.
Rahimi-Eichi H, Ojha U, Baronti F, Chow MY. Battery management system: An overview of its application in the smart grid and electric.
11.
vehicles. IEEE industrial electronics magazine. 2013 Jun 14;7(2):4-16. https://doi.org/10.1109/MIE.20....
12.
Severson KA, Attia PM, Jin N, Perkins N, Jiang B, Yang Z, Chen MH, Aykol M, Herring PK, Fraggedakis D, Bazant MZ. Data-driven.
13.
prediction of battery cycle life before capacity degradation. Nature Energy. 2019 May;4(5):383-91. https://doi.org/10.1038/s41560...-.
15.
Safavi V, Mohammadi Vaniar A, Bazmohammadi N, Vasquez JC, Guerrero JM. Battery Remaining Useful Life Prediction Using Machine.
16.
Learning Models: A Comparative Study. Information. 2024 Feb 22;15(3):124. https://doi.org/10.3390/info15....
17.
Wang, Z., Shangguan, W., Peng, C., and Cai, B.. Similarity Based Remaining Useful Life Prediction for Lithium-ion Battery under Small.
18.
Sample Situation Based on Data Augmentation. Eksploatacja i Niezawodność – Maintenance and Reliability, 2024 26(1).
20.
Fang P, Sui X, Zhang A, Wang D, Yin L. Fusion model based RUL prediction method of lithium-ion battery under working conditions.
21.
Eksploatacja i Niezawodność – Maintenance and Reliability. 2024;26(3). https://doi.org/10.17531/ein/1....
22.
Wang Z, Liu N, Chen C, Guo Y. Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries. Information Sciences. 2023.
24.
Li X, Ma Y, Zhu J. An online dual filters RUL prediction method of lithium-ion battery based on unscented particle filter and least squares.
27.
Al-Greer M, Bashir I. Physics-based model informed smooth particle filter for remaining useful life prediction of lithium-ion battery.
29.
Li C, Zhang H, Ding P, Yang S, Bai Y. Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and.
30.
perspectives. Renewable and Sustainable Energy Reviews. 2023 Sep 1;184:113576, https://doi.org/10.1016/j.rser....
31.
Liu D, Li L, Song Y, Wu L, Peng Y. Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions.
32.
International Journal of Electrical Power & Energy Systems. 2019 Sep 1;110:48-61, https://doi.org/10.1016/j.ijep....
33.
Guo X, Ou S, Jiang M, Gao Y, Xu J, Cai Z. A New Sparse Kernel RLS Algorithm for Identification of Nonlinear Systems. IEEE Access.
35.
Liu W, Pokharel PP, Principe JC. The kernel least-mean-square algorithm. IEEE Transactions on signal processing. 2008 Jan 16;56(2):543-.
37.
Engel Y, Mannor S, Meir R. The kernel recursive least-squares algorithm. IEEE Transactions on signal processing. 2004 Jul 19;52(8):2275-.
39.
Van Vaerenbergh S, Via J, Santamaría I. A sliding-window kernel RLS algorithm and its application to nonlinear channel identification.
40.
In2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings 2006 May 14 (Vol. 5, pp. 789-792). IEEE,.
42.
Liu W, Park I, Wang Y, Principe JC. Extended kernel recursive least squares algorithm. IEEE Transactions on Signal Processing. 2009.
44.
Chen B, Zhao S, Zhu P, Principe JC. Quantized kernel recursive least squares algorithm. IEEE transactions on neural networks and learning.
46.
Van Vaerenbergh S, Santamaría I, Liu W, Príncipe JC. Fixed-budget kernel recursive least-squares. In2010 IEEE International Conference.
47.
on Acoustics, Speech and Signal Processing 2010 Mar 14 (pp. 1882-1885). IEEE, https://doi.org/10.1109/ICASSP....
48.
Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural computation. 2006 Jul 1;18(7):1527-54,.
50.
Cheng Y, Wan S, Choo KK. Deep belief network for meteorological time series prediction in the Internet of Things. IEEE internet of things.
52.
Hu S, Xiang Y, Huo D, Jawad S, Liu J. An improved deep belief network based hybrid forecasting method for wind power. Energy. 2021.
54.
Cao M, Zhang T, Wang J, Liu Y. A deep belief network approach to remaining capacity estimation for lithium-ion batteries based on.
55.
charging process features. Journal of Energy Storage. 2022 Apr 1;48:103825, https://doi.org/10.1016/j.est.....
56.
Sun X, Wang G, Xu L, Yuan H, Yousefi N. Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved.
57.
archimedes optimization algorithm. Energy. 2021 Dec 15;237:121532, https://doi.org/10.1016/j.ener....
58.
Arasaratnam I, Haykin S. Cubature kalman filters. IEEE Transactions on automatic control. 2009 May 27;54(6):1254-69,.
60.
Han M, Xu M, Liu X, Wang X. Online multivariate time series prediction using SCKF-γESN model. Neurocomputing. 2015 Jan 5;147:315-.
CITATIONS (1):
1.
Commercial Drone Deliveries: Evolution, Applications, and Market Dynamics
Mateusz Mazur, Anna Borucka
2025 13th International Conference on Traffic and Logistic Engineering (ICTLE)
Mateusz Mazur, Anna Borucka
2025 13th International Conference on Traffic and Logistic Engineering (ICTLE)
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
