Data smoothing in Fuzzy Entropy-based Battery State of Health Estimation

Xin Sui, Shan He, Xinrong Huang, Remus Teodorescu, Daniel-Ioan Stroe

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1 Citationer (Scopus)
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Abstract

To ensure the reliable operation of the batteries and maximize their service lifetime, it is important to have accurate knowledge of their state of health (SOH). Using data-driven methods to estimate the SOH is extensively studied and the feature data plays an important role in such methods. As fuzzy entropy (FE) can capture the variation of the voltage during the battery aging process, it can be used as a feature. In this paper, in order to reduce the noise from raw feature data, six smoothing methods are introduced to pre-process the FE. Furthermore, the relationship between the smoothed feature and SOH is established by support vector machine and Gaussian process regression. The comparison results show that adding a simply feature smoothing step before the model training can improve the SOH estimation performance. Finally, the effectiveness of the proposed method is verified by experimental results.
OriginalsprogEngelsk
TitelIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society
Antal sider6
ForlagIEEE
Publikationsdatonov. 2020
Sider1779-1784
ISBN (Trykt)978-1-7281-5415-2
ISBN (Elektronisk)978-1-7281-5414-5
DOI
StatusUdgivet - nov. 2020
BegivenhedIECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society - Online
Varighed: 18 okt. 202021 okt. 2020
https://www.iecon2020.org/

Konference

KonferenceIECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society
LokationOnline
Periode18/10/202021/10/2020
Internetadresse
NavnProceedings of the Annual Conference of the IEEE Industrial Electronics Society
ISSN1553-572X

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