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

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

doi:10.1109/IECON43393.2020.9255281

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

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

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

1 Citation (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.

Original language	English
Title of host publication	IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society
Number of pages	6
Publisher	IEEE
Publication date	Nov 2020
Pages	1779-1784
ISBN (Print)	978-1-7281-5415-2
ISBN (Electronic)	978-1-7281-5414-5
DOIs	https://doi.org/10.1109/IECON43393.2020.9255281
Publication status	Published - Nov 2020
Event	IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society - Online Duration: 18 Oct 2020 → 21 Oct 2020 https://www.iecon2020.org/

Conference

Conference	IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society
Location	Online
Period	18/10/2020 → 21/10/2020
Internet address	https://www.iecon2020.org/

Series	Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
ISSN	1553-572X

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10.1109/IECON43393.2020.9255281

Data smoothing in Fuzzy Entropy-based Battery State of Health EstimationAccepted author manuscript, 462 KB

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1 Citations
1 PhD thesis

The Effects of Pulsed Charging Current on the Performance and Lifetime of Lithium-Ion Batteries
Huang, X., 2021, Aalborg Universitetsforlag. 77 p.
Research output: PhD thesis

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Cite this

@inproceedings{b96cc9fc1d92474d805947bb543c3437,

title = "Data smoothing in Fuzzy Entropy-based Battery State of Health Estimation",

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.",

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

year = "2020",

month = nov,

doi = "10.1109/IECON43393.2020.9255281",

language = "English",

isbn = "978-1-7281-5415-2",

series = "Proceedings of the Annual Conference of the IEEE Industrial Electronics Society",

publisher = "IEEE",

pages = "1779--1784",

booktitle = "IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society",

address = "United States",

note = "IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society ; Conference date: 18-10-2020 Through 21-10-2020",

url = "https://www.iecon2020.org/",

}

Sui, X, He, S, Huang, X, Teodorescu, R & Stroe, D-I 2020, Data smoothing in Fuzzy Entropy-based Battery State of Health Estimation. in IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, pp. 1779-1784, IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society, 18/10/2020. https://doi.org/10.1109/IECON43393.2020.9255281

Data smoothing in Fuzzy Entropy-based Battery State of Health Estimation. / Sui, Xin; He, Shan; Huang, Xinrong et al.
IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2020. p. 1779-1784 (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society).

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

TY - GEN

T1 - Data smoothing in Fuzzy Entropy-based Battery State of Health Estimation

AU - Sui, Xin

AU - He, Shan

AU - Huang, Xinrong

AU - Teodorescu, Remus

AU - Stroe, Daniel-Ioan

PY - 2020/11

Y1 - 2020/11

N2 - 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.

AB - 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.

U2 - 10.1109/IECON43393.2020.9255281

DO - 10.1109/IECON43393.2020.9255281

M3 - Article in proceeding

SN - 978-1-7281-5415-2

T3 - Proceedings of the Annual Conference of the IEEE Industrial Electronics Society

SP - 1779

EP - 1784

BT - IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE

T2 - IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society

Y2 - 18 October 2020 through 21 October 2020

ER -

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

Abstract

Conference

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Research output

The Effects of Pulsed Charging Current on the Performance and Lifetime of Lithium-Ion Batteries

Cite this