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Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM
Joint Authors
Zhang, Chaolong
Yuan, Lifeng
Xiang, Sheng
Wang, Jinping
He, Yigang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-08-30
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Lithium-ion batteries are widely used in many electronic systems.
Therefore, it is significantly important to estimate the lithium-ion battery’s remaining useful life (RUL), yet very difficult.
One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution.
In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries.
Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise.
Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data.
An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately.
American Psychological Association (APA)
Zhang, Chaolong& He, Yigang& Yuan, Lifeng& Xiang, Sheng& Wang, Jinping. 2015. Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1057781
Modern Language Association (MLA)
Zhang, Chaolong…[et al.]. Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1057781
American Medical Association (AMA)
Zhang, Chaolong& He, Yigang& Yuan, Lifeng& Xiang, Sheng& Wang, Jinping. Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1057781
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057781