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

Xin Sui; Shan He; Daniel-Ioan Stroe; Remus Teodorescu

doi:10.1109/APEC39645.2020.9124327

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

Xin Sui^*, Shan He, Daniel-Ioan Stroe, Remus Teodorescu

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

7 Citations (Scopus)

70 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	2020 IEEE Applied Power Electronics Conference and Exposition (APEC)
Number of pages	6
Publisher	IEEE Press
Publication date	2020
Pages	3424-3429
Article number	9124327
ISBN (Electronic)	9781728148298
DOIs	https://doi.org/10.1109/APEC39645.2020.9124327
Publication status	Published - 2020
Event	2020 IEEE Applied Power Electronics Conference and Exposition (APEC) - New Orleans, LA, United States Duration: 15 Mar 2020 → 19 Mar 2020

Conference

Conference	2020 IEEE Applied Power Electronics Conference and Exposition (APEC)
Country/Territory	United States
City	New Orleans, LA
Period	15/03/2020 → 19/03/2020

Series	IEEE Applied Power Electronics Conference and Exposition (APEC)
ISSN	1048-2334

Keywords

Empirical mode decomposition denoising
Lithium-ion battery
Sample entropy
State of health

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Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector MachineAccepted author manuscript, 1.37 MB

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@inproceedings{1563ddf35671424886922dcac06b232e,

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

abstract = "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.",

keywords = "Empirical mode decomposition denoising, Lithium-ion battery, Sample entropy, State of health",

author = "Xin Sui and Shan He and Daniel-Ioan Stroe and Remus Teodorescu",

year = "2020",

doi = "10.1109/APEC39645.2020.9124327",

language = "English",

series = "IEEE Applied Power Electronics Conference and Exposition (APEC)",

publisher = "IEEE Press",

pages = "3424--3429",

booktitle = "2020 IEEE Applied Power Electronics Conference and Exposition (APEC)",

note = "2020 IEEE Applied Power Electronics Conference and Exposition (APEC) ; Conference date: 15-03-2020 Through 19-03-2020",

}

Sui, X, He, S, Stroe, D-I & Teodorescu, R 2020, Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector Machine. in 2020 IEEE Applied Power Electronics Conference and Exposition (APEC)., 9124327, IEEE Press, IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 3424-3429, 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), New Orleans, LA, United States, 15/03/2020. https://doi.org/10.1109/APEC39645.2020.9124327

Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector Machine. / Sui, Xin; He, Shan; Stroe, Daniel-Ioan et al.
2020 IEEE Applied Power Electronics Conference and Exposition (APEC). IEEE Press, 2020. p. 3424-3429 9124327 (IEEE Applied Power Electronics Conference and Exposition (APEC)).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector Machine

AU - Sui, Xin

AU - He, Shan

AU - Stroe, Daniel-Ioan

AU - Teodorescu, Remus

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Empirical mode decomposition denoising

KW - Lithium-ion battery

KW - Sample entropy

KW - State of health

UR - https://ieeexplore.ieee.org/document/9124327

U2 - 10.1109/APEC39645.2020.9124327

DO - 10.1109/APEC39645.2020.9124327

M3 - Article in proceeding

T3 - IEEE Applied Power Electronics Conference and Exposition (APEC)

SP - 3424

EP - 3429

BT - 2020 IEEE Applied Power Electronics Conference and Exposition (APEC)

PB - IEEE Press

T2 - 2020 IEEE Applied Power Electronics Conference and Exposition (APEC)

Y2 - 15 March 2020 through 19 March 2020

ER -

Sui X, He S, Stroe DI , Teodorescu R. Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector Machine. In 2020 IEEE Applied Power Electronics Conference and Exposition (APEC). IEEE Press. 2020. p. 3424-3429. 9124327. (IEEE Applied Power Electronics Conference and Exposition (APEC)). doi: 10.1109/APEC39645.2020.9124327

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

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