Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector Machine

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Abstrakt

Accurate knowledge of the state of health (SOH) is important for battery management system to maintain safe operation of batteries and extend their lifetime. In order to improve the accuracy in estimating the SOH of a battery, a new method based on empirical mode decomposition sample entropy (EMDSE) and support vector machine (SVM) is proposed in this paper. Compared with the tradition SE-based method, EMD is used to filter the noise of the original signal. Then the EMDSE as a new feature is used for the SVM training, and the potential relationship between the SOH and the EMDSE is established. Finally, the effectiveness of the proposed method is verified by experimental results.
OriginalsprogEngelsk
Titel2020 IEEE Applied Power Electronics Conference and Exposition (APEC)
Antal sider6
ForlagIEEE Press
Publikationsdato2020
Sider3424-3429
Artikelnummer9124327
ISBN (Elektronisk)9781728148298
DOI
StatusUdgivet - 2020
Begivenhed2020 IEEE Applied Power Electronics Conference and Exposition (APEC) - New Orleans, LA, USA
Varighed: 15 mar. 202019 mar. 2020

Konference

Konference2020 IEEE Applied Power Electronics Conference and Exposition (APEC)
LandUSA
ByNew Orleans, LA
Periode15/03/202019/03/2020
NavnIEEE Applied Power Electronics Conference and Exposition (APEC)
ISSN1048-2334

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