Fuzzy Entropy-Based State of Health Estimation of LiFePO4Batteries Considering Temperature Variation

Xin Sui; Shan He; Jinhao Meng; Remus Teodorescu; Daniel Ioan Stroe

doi:10.1109/ECCE44975.2020.9236267

Fuzzy Entropy-Based State of Health Estimation of LiFePO₄Batteries Considering Temperature Variation

Xin Sui
, Shan He
, Jinhao Meng
, Remus Teodorescu
, Daniel Ioan Stroe

Aalborg University
Sichuan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Sample entropy (SE) has been used as a feature to estimate the state of health (SOH) of batteries as it can capture the voltage variation during battery degradation. However, because the Heaviside function is used to access similarity in the definition of SE, SE is sensitive to parameter selection. Hence, the SE shows an obvious change when the battery is aged at different conditions (e.g., temperatures), leading to a decrease in the estimation accuracy. By introducing the concept of fuzzy membership, the generalized version of SE, fuzzy entropy (FE) is weak influenced by parameters and test condition. Therefore, the FE is proposed as a feature to estimate the SOH of battery in terms of aging temperature variation. The FE-SOH is used as the input-output data pair of support vector machine, then the single-temperature model, full temperature model, and partial-temperature model are established. Compared with the SE-based method, FE-based method not only has better estimation accuracy, but also decreases the dependence on the size of training data. Finally, the effectiveness of the proposed method is verified using experimental results.

Original language	English
Title of host publication	ECCE 2020 - IEEE Energy Conversion Congress and Exposition
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4401-4406
Number of pages	6
ISBN (Electronic)	9781728158266
DOIs	https://doi.org/10.1109/ECCE44975.2020.9236267
State	Published - 11 Oct 2020
Externally published	Yes
Event	12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 - Virtual, Detroit, United States Duration: 11 Oct 2020 → 15 Oct 2020

Publication series

Name	ECCE 2020 - IEEE Energy Conversion Congress and Exposition

Conference

Conference	12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020
Country/Territory	United States
City	Virtual, Detroit
Period	11/10/20 → 15/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Aging temperature variation
Fuzzy entropy
Lithium-ion battery
Sample entropy
State of health estimation
Support vector machine

Access to Document

10.1109/ECCE44975.2020.9236267

Cite this

Sui, X., He, S., Meng, J., Teodorescu, R., & Stroe, D. I. (2020). Fuzzy Entropy-Based State of Health Estimation of LiFePO₄Batteries Considering Temperature Variation. In ECCE 2020 - IEEE Energy Conversion Congress and Exposition (pp. 4401-4406). Article 9236267 (ECCE 2020 - IEEE Energy Conversion Congress and Exposition). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE44975.2020.9236267

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title = "Fuzzy Entropy-Based State of Health Estimation of LiFePO4Batteries Considering Temperature Variation",

abstract = "Sample entropy (SE) has been used as a feature to estimate the state of health (SOH) of batteries as it can capture the voltage variation during battery degradation. However, because the Heaviside function is used to access similarity in the definition of SE, SE is sensitive to parameter selection. Hence, the SE shows an obvious change when the battery is aged at different conditions (e.g., temperatures), leading to a decrease in the estimation accuracy. By introducing the concept of fuzzy membership, the generalized version of SE, fuzzy entropy (FE) is weak influenced by parameters and test condition. Therefore, the FE is proposed as a feature to estimate the SOH of battery in terms of aging temperature variation. The FE-SOH is used as the input-output data pair of support vector machine, then the single-temperature model, full temperature model, and partial-temperature model are established. Compared with the SE-based method, FE-based method not only has better estimation accuracy, but also decreases the dependence on the size of training data. Finally, the effectiveness of the proposed method is verified using experimental results.",

keywords = "Aging temperature variation, Fuzzy entropy, Lithium-ion battery, Sample entropy, State of health estimation, Support vector machine",

author = "Xin Sui and Shan He and Jinhao Meng and Remus Teodorescu and Stroe, \{Daniel Ioan\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 ; Conference date: 11-10-2020 Through 15-10-2020",

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Sui, X, He, S, Meng, J, Teodorescu, R & Stroe, DI 2020, Fuzzy Entropy-Based State of Health Estimation of LiFePO₄Batteries Considering Temperature Variation. in ECCE 2020 - IEEE Energy Conversion Congress and Exposition., 9236267, ECCE 2020 - IEEE Energy Conversion Congress and Exposition, Institute of Electrical and Electronics Engineers Inc., pp. 4401-4406, 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020, Virtual, Detroit, United States, 11/10/20. https://doi.org/10.1109/ECCE44975.2020.9236267

Fuzzy Entropy-Based State of Health Estimation of LiFePO₄Batteries Considering Temperature Variation. / Sui, Xin; He, Shan; Meng, Jinhao et al.
ECCE 2020 - IEEE Energy Conversion Congress and Exposition. Institute of Electrical and Electronics Engineers Inc., 2020. p. 4401-4406 9236267 (ECCE 2020 - IEEE Energy Conversion Congress and Exposition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Sui, Xin

AU - He, Shan

AU - Meng, Jinhao

AU - Teodorescu, Remus

AU - Stroe, Daniel Ioan

PY - 2020/10/11

Y1 - 2020/10/11

N2 - Sample entropy (SE) has been used as a feature to estimate the state of health (SOH) of batteries as it can capture the voltage variation during battery degradation. However, because the Heaviside function is used to access similarity in the definition of SE, SE is sensitive to parameter selection. Hence, the SE shows an obvious change when the battery is aged at different conditions (e.g., temperatures), leading to a decrease in the estimation accuracy. By introducing the concept of fuzzy membership, the generalized version of SE, fuzzy entropy (FE) is weak influenced by parameters and test condition. Therefore, the FE is proposed as a feature to estimate the SOH of battery in terms of aging temperature variation. The FE-SOH is used as the input-output data pair of support vector machine, then the single-temperature model, full temperature model, and partial-temperature model are established. Compared with the SE-based method, FE-based method not only has better estimation accuracy, but also decreases the dependence on the size of training data. Finally, the effectiveness of the proposed method is verified using experimental results.

AB - Sample entropy (SE) has been used as a feature to estimate the state of health (SOH) of batteries as it can capture the voltage variation during battery degradation. However, because the Heaviside function is used to access similarity in the definition of SE, SE is sensitive to parameter selection. Hence, the SE shows an obvious change when the battery is aged at different conditions (e.g., temperatures), leading to a decrease in the estimation accuracy. By introducing the concept of fuzzy membership, the generalized version of SE, fuzzy entropy (FE) is weak influenced by parameters and test condition. Therefore, the FE is proposed as a feature to estimate the SOH of battery in terms of aging temperature variation. The FE-SOH is used as the input-output data pair of support vector machine, then the single-temperature model, full temperature model, and partial-temperature model are established. Compared with the SE-based method, FE-based method not only has better estimation accuracy, but also decreases the dependence on the size of training data. Finally, the effectiveness of the proposed method is verified using experimental results.

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KW - Fuzzy entropy

KW - Lithium-ion battery

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T3 - ECCE 2020 - IEEE Energy Conversion Congress and Exposition

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BT - ECCE 2020 - IEEE Energy Conversion Congress and Exposition

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020

Y2 - 11 October 2020 through 15 October 2020

ER -

Sui X, He S, Meng J, Teodorescu R, Stroe DI. Fuzzy Entropy-Based State of Health Estimation of LiFePO₄Batteries Considering Temperature Variation. In ECCE 2020 - IEEE Energy Conversion Congress and Exposition. Institute of Electrical and Electronics Engineers Inc. 2020. p. 4401-4406. 9236267. (ECCE 2020 - IEEE Energy Conversion Congress and Exposition). doi: 10.1109/ECCE44975.2020.9236267