Evidence map›Paper›PMID 26413090›Full record

ArticleComputational intelligence and neuroscience2015

Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.

Chaolong Zhang, Yigang He, Lifeng Yuan, Sheng Xiang, Jinping Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational intelligence and neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chaolong ZhangSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China ; School of Physics and Electronic Engineering, Anqing Normal University, Anqing 246011, China.
Yigang HeSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
Lifeng YuanSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
Sheng XiangSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
Jinping WangSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AlgorithmsBiological EvolutionElectric Power SuppliesWavelet AnalysisHumansLithiumModels, TheoreticalSupport Vector MachineLithium

Identifiers

PMID26413090
PMCPMC4568058

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.