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Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM
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 le...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4568058/ https://www.ncbi.nlm.nih.gov/pubmed/26413090 http://dx.doi.org/10.1155/2015/918305 |
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author | Zhang, Chaolong He, Yigang Yuan, Lifeng Xiang, Sheng Wang, Jinping |
author_facet | Zhang, Chaolong He, Yigang Yuan, Lifeng Xiang, Sheng Wang, Jinping |
author_sort | Zhang, Chaolong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4568058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-45680582015-09-27 Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM Zhang, Chaolong He, Yigang Yuan, Lifeng Xiang, Sheng Wang, Jinping Comput Intell Neurosci Research Article 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. Hindawi Publishing Corporation 2015 2015-08-30 /pmc/articles/PMC4568058/ /pubmed/26413090 http://dx.doi.org/10.1155/2015/918305 Text en Copyright © 2015 Chaolong Zhang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Chaolong He, Yigang Yuan, Lifeng Xiang, Sheng Wang, Jinping Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM |
title | Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM |
title_full | Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM |
title_fullStr | Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM |
title_full_unstemmed | Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM |
title_short | Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM |
title_sort | prognostics of lithium-ion batteries based on wavelet denoising and de-rvm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4568058/ https://www.ncbi.nlm.nih.gov/pubmed/26413090 http://dx.doi.org/10.1155/2015/918305 |
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