Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries
Source abstract
Abstract
Monitoring cycle life can provide a prediction of the remaining battery life. To improve the prediction accuracy of lithium-ion battery capacity degradation, we propose a hybrid long short-term memory recurrent neural network model with an attention mechanism. The hyper-parameters of the proposed model are also optimized by a differential evolution algorithm. Using public battery datasets, the proposed model is compared to some published models, and it gives better prediction performance in terms of mean absolute percentage error and root mean square error. In addition, the proposed model can achieve higher prediction accuracy of battery end of life.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, Mettu University, Mettu P.O. Box 318, Ethiopia
Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
References (33)
Show source-provided references
1. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications10.1016/j.jpowsour.2013.05.040 ↗
2. Wu, L., Fu, X., and Guan, Y. (2016). Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies. Appl. Sci., 6.10.3390/app6060166 ↗
3. A review on prognostics approaches for remaining useful life of lithium-ion battery10.1088/1755-1315/93/1/012040 ↗
4. Omariba, Z.B., Zhang, L., and Sun, D. (2018). Review on health management system for lithium-ion batteries of electric vehicles. Electronics, 7.10.3390/electronics7050072 ↗
5. Estimation of Li-ion battery state of health based on multilayer perceptron: As an EV application10.1016/j.ifacol.2018.11.734 ↗
6. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries10.1109/TVT.2018.2805189 ↗
7. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks10.1016/j.est.2018.12.011 ↗
8. Remaining useful life prediction for lithium-ion battery: A deep learning approach10.1109/ACCESS.2018.2858856 ↗
9. The remaining useful life estimation of lithium-ion battery based on improved extreme learning machine algorithm10.20964/2018.05.84 ↗
10. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression10.1109/TIE.2017.2782224 ↗
11. A hybrid model based on support vector regression and differential evolution for remaining useful lifetime prediction of lithium-ion batteries10.1016/j.jpowsour.2018.08.073 ↗
12. State-of-health estimation for the lithium-ion battery based on support vector regression10.1016/j.apenergy.2017.08.096 ↗
13. A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries10.1016/j.microrel.2018.04.007 ↗
14. Wang, Z., Zeng, S., Guo, J., and Qin, T. (2018). Remaining capacity estimation of lithium-ion batteries based on the constant voltage charging profile. PLoS ONE, 13.10.1371/journal.pone.0200169 ↗
15. Online capacity estimation of lithium-ion batteries based on novel feature extraction and adaptive multi-kernel relevance vector machine10.3390/en81112320 ↗
16. Lithium-ion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning10.1016/j.measurement.2014.11.031 ↗
17. Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm10.1016/j.cja.2017.11.010 ↗
18. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve10.1016/j.jpowsour.2018.03.015 ↗
19. State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble10.1016/j.ress.2018.02.022 ↗
20. Peng, Y., Hou, Y., Song, Y., Pang, J., and Liu, D. (2018). Lithium-ion battery prognostics with hybrid Gaussian process function regression. Energies, 11.10.3390/en11061420 ↗
21. Gaussian process regression for in-situ capacity estimation of lithium-ion batteries10.1109/TII.2018.2794997 ↗
22. A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter10.1016/j.microrel.2015.02.025 ↗
23. Random forest regression for online capacity estimation of lithium-ion batteries10.1016/j.apenergy.2018.09.182 ↗
24. Remaining useful battery life prediction for UAVs based on machine learning10.1016/j.ifacol.2017.08.863 ↗
25. A machine learning method for predicting driving range of battery electric vehicles10.1155/2019/4109148 ↗
26. Saha, B., and Goebel, K. (2007). Battery Data Set, NASA AMES Prognostics Data Repository.
27. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique10.1016/j.microrel.2012.12.004 ↗
28. Birkl, C. (2017). Oxford Battery Degradation Dataset 1, University of Oxford.
29. Data-driven prediction of battery cycle life before capacity degradation10.1038/s41560-019-0356-8 ↗
30. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
31. Raffel, C., and Ellis, D.P. (2015). Feed-forward networks with attention can solve some long-term memory problem. arXiv.
32. Bi-directional long short-term memory recurrent neural network with attention for stack voltage degradation from proton exchange membrane fuel cells10.1016/j.jpowsour.2020.228170 ↗
33. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces10.1023/A:1008202821328 ↗