State of Health Estimation of Lithium-Ion Batteries from Charging Data: A Machine Learning Method

Zuolu Wang; Guojin Feng; Dong Zhen; Fengshou Gu; Andrew D. Ball

doi:10.1007/978-3-030-99075-6_57

State of Health Estimation of Lithium-Ion Batteries from Charging Data: A Machine Learning Method

Zuolu Wang, Guojin Feng, Dong Zhen, Fengshou Gu, Andrew D. Ball

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

Abstract

Accurate state of health (SOH) estimation of the lithium-ion battery plays an important role in ensuring the reliability and safety of the battery management system (BMS). The data-driven method based on the selection of degradation features can be effectively applied to SOH estimation. In practice, lithium batteries often work in complex discharge conditions, but they are charged under constant current (CC) conditions. Therefore, the suitable degradation features of the battery are extracted in this work for accurate SOH estimation. First, the degradation features are summarized and extracted from the CC charging data. Second, the Pearson correlation coefficient is utilized to quantify the relationship between the extracted degradation features and the battery SOH, thus determining the most influential degradation feature. Finally, the long short term memory (LSTM) is used for model training and SOH estimation based on the selected feature. The results show that LSTM model can give reliable and accurate SOH estimation with R² of 1 and lower mean absolute error (MAE) and maximum error (MAX).

Original language	English
Title of host publication	Proceedings of IncoME-VI and TEPEN 2021
Subtitle of host publication	Performance Engineering and Maintenance Engineering
Editors	Hao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
Publisher	Springer, Cham
Pages	707-719
Number of pages	13
Volume	117
ISBN (Electronic)	9783030990756
ISBN (Print)	9783030990749
DOIs	https://doi.org/10.1007/978-3-030-99075-6_57
Publication status	Published - 18 Sep 2022
Event	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Hebei University of Technology, Tianjin, China Duration: 20 Oct 2021 → 23 Oct 2021 Conference number: 6 https://tepen.net/conference/tepen2021/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	117
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Abbreviated title	TEPEN-2021 and IncoME-VI
Country/Territory	China
City	Tianjin
Period	20/10/21 → 23/10/21
Internet address	https://tepen.net/conference/tepen2021/

UN SDGs

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

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Cite this

Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. D. (2022). State of Health Estimation of Lithium-Ion Batteries from Charging Data: A Machine Learning Method. In H. Zhang, G. Feng, H. Wang, F. Gu, & J. K. Sinha (Eds.), Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering (Vol. 117, pp. 707-719). (Mechanisms and Machine Science; Vol. 117). Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_57

Wang, Zuolu ; Feng, Guojin ; Zhen, Dong et al. / State of Health Estimation of Lithium-Ion Batteries from Charging Data : A Machine Learning Method. Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. editor / Hao Zhang ; Guojin Feng ; Hongjun Wang ; Fengshou Gu ; Jyoti K. Sinha. Vol. 117 Springer, Cham, 2022. pp. 707-719 (Mechanisms and Machine Science).

@inproceedings{132d4df72fd042309c1c7ee9ef8d1565,

title = "State of Health Estimation of Lithium-Ion Batteries from Charging Data: A Machine Learning Method",

abstract = "Accurate state of health (SOH) estimation of the lithium-ion battery plays an important role in ensuring the reliability and safety of the battery management system (BMS). The data-driven method based on the selection of degradation features can be effectively applied to SOH estimation. In practice, lithium batteries often work in complex discharge conditions, but they are charged under constant current (CC) conditions. Therefore, the suitable degradation features of the battery are extracted in this work for accurate SOH estimation. First, the degradation features are summarized and extracted from the CC charging data. Second, the Pearson correlation coefficient is utilized to quantify the relationship between the extracted degradation features and the battery SOH, thus determining the most influential degradation feature. Finally, the long short term memory (LSTM) is used for model training and SOH estimation based on the selected feature. The results show that LSTM model can give reliable and accurate SOH estimation with R2 of 1 and lower mean absolute error (MAE) and maximum error (MAX).",

keywords = "Feature extraction, Lithium-ion battery, Long short term memory, State of health",

author = "Zuolu Wang and Guojin Feng and Dong Zhen and Fengshou Gu and Ball, {Andrew D.}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, TEPEN-2021 and IncoME-VI ; Conference date: 20-10-2021 Through 23-10-2021",

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Wang, Z, Feng, G, Zhen, D, Gu, F & Ball, AD 2022, State of Health Estimation of Lithium-Ion Batteries from Charging Data: A Machine Learning Method. in H Zhang, G Feng, H Wang, F Gu & JK Sinha (eds), Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. vol. 117, Mechanisms and Machine Science, vol. 117, Springer, Cham, pp. 707-719, 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, Tianjin, China, 20/10/21. https://doi.org/10.1007/978-3-030-99075-6_57

State of Health Estimation of Lithium-Ion Batteries from Charging Data : A Machine Learning Method. / Wang, Zuolu; Feng, Guojin; Zhen, Dong et al.

Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. ed. / Hao Zhang; Guojin Feng; Hongjun Wang; Fengshou Gu; Jyoti K. Sinha. Vol. 117 Springer, Cham, 2022. p. 707-719 (Mechanisms and Machine Science; Vol. 117).

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

TY - GEN

T1 - State of Health Estimation of Lithium-Ion Batteries from Charging Data

T2 - 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021

AU - Wang, Zuolu

AU - Feng, Guojin

AU - Zhen, Dong

AU - Gu, Fengshou

AU - Ball, Andrew D.

N1 - Conference code: 6

PY - 2022/9/18

Y1 - 2022/9/18

N2 - Accurate state of health (SOH) estimation of the lithium-ion battery plays an important role in ensuring the reliability and safety of the battery management system (BMS). The data-driven method based on the selection of degradation features can be effectively applied to SOH estimation. In practice, lithium batteries often work in complex discharge conditions, but they are charged under constant current (CC) conditions. Therefore, the suitable degradation features of the battery are extracted in this work for accurate SOH estimation. First, the degradation features are summarized and extracted from the CC charging data. Second, the Pearson correlation coefficient is utilized to quantify the relationship between the extracted degradation features and the battery SOH, thus determining the most influential degradation feature. Finally, the long short term memory (LSTM) is used for model training and SOH estimation based on the selected feature. The results show that LSTM model can give reliable and accurate SOH estimation with R2 of 1 and lower mean absolute error (MAE) and maximum error (MAX).

AB - Accurate state of health (SOH) estimation of the lithium-ion battery plays an important role in ensuring the reliability and safety of the battery management system (BMS). The data-driven method based on the selection of degradation features can be effectively applied to SOH estimation. In practice, lithium batteries often work in complex discharge conditions, but they are charged under constant current (CC) conditions. Therefore, the suitable degradation features of the battery are extracted in this work for accurate SOH estimation. First, the degradation features are summarized and extracted from the CC charging data. Second, the Pearson correlation coefficient is utilized to quantify the relationship between the extracted degradation features and the battery SOH, thus determining the most influential degradation feature. Finally, the long short term memory (LSTM) is used for model training and SOH estimation based on the selected feature. The results show that LSTM model can give reliable and accurate SOH estimation with R2 of 1 and lower mean absolute error (MAE) and maximum error (MAX).

KW - Feature extraction

KW - Lithium-ion battery

KW - Long short term memory

KW - State of health

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SN - 9783030990749

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T3 - Mechanisms and Machine Science

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BT - Proceedings of IncoME-VI and TEPEN 2021

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A2 - Feng, Guojin

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A2 - Gu, Fengshou

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PB - Springer, Cham

Y2 - 20 October 2021 through 23 October 2021

ER -

Wang Z, Feng G, Zhen D, Gu F , Ball AD. State of Health Estimation of Lithium-Ion Batteries from Charging Data: A Machine Learning Method. In Zhang H, Feng G, Wang H, Gu F, Sinha JK, editors, Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. Vol. 117. Springer, Cham. 2022. p. 707-719. (Mechanisms and Machine Science). Epub 2022 Sep 18. doi: 10.1007/978-3-030-99075-6_57

State of Health Estimation of Lithium-Ion Batteries from Charging Data: A Machine Learning Method

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