State of Health Estimation for Lithium-ion Battery Using Fuzzy Entropy and Support Vector Machine

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2 Citationer (Scopus)

Abstract

In this paper, fuzzy entropy (FE), as a new feature, is applied for LiFeO4 battery state of health (SOH) estimation. Compared with sample entropy, FE introduces the exponential function to measure the similarity of voltage vectors and the mean of the match templates is removed. As a result, FE can capture the variation of voltage during the battery degradation more efficiently in terms of the parameter selection, data noise, and data size. Then the FE-SOH mapping is established by combining FE with support vector machine, and the effectiveness of the proposed method is verified by experimental results.

OriginalsprogEngelsk
Titel2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC 2020-ECCE Asia)
Antal sider6
ForlagIEEE
Publikationsdato2020
Sider1417-1422
Artikelnummer9368182
ISBN (Trykt)978-1-7281-5302-5
ISBN (Elektronisk)978-1-7281-5301-8
DOI
StatusUdgivet - 2020
Begivenhed2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia) - Nanjing, Kina
Varighed: 29 nov. 20202 dec. 2020

Konference

Konference2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia)
Land/OmrådeKina
ByNanjing
Periode29/11/202002/12/2020

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