State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length

Si Joong Kim; Seon Hyeog Kim; Hyeong Min Lee; Sue Hyang Lim; Gu Young Kwon; Yong June Shin

doi:10.1109/ISIE45063.2020.9152544

State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length

Si Joong Kim, Seon Hyeog Kim, Hyeong Min Lee, Sue Hyang Lim, Gu Young Kwon, Yong June Shin

Department of Electrical and Electronic Engineering

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

16 Citations (Scopus)

Abstract

This paper presents a lithium-ion battery state-of-health (SOH) estimation method based on a long short-term memory neural network. The proposed algorithm uses multi-input for the current state of the battery, which are voltage, current, temperature and state-of-charge (SOC). In addition, it reflects the historical state of the battery such as difference of current and temperature by varying the input sequence length, for a more accurate SOH estimation. To verify the proposed algorithm, the accuracy value when using the error function is checked. Then, the algorithm selects the appropriate sequence length and analyzes the system. The system improves estimation accuracy through the feedback process. Furthermore, the proposed method is compared to other learning methods to support the validation. The results highlight the selection of the appropriate sequence length can improve the accuracy of the estimation and show that the battery SOH estimation with the optimal sequence length is great effect on the performance through RMSE 0.1425, MAE 0.1084, and MAPE 0.8658%.

Original language	English
Title of host publication	2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1336-1341
Number of pages	6
ISBN (Electronic)	9781728156354
DOIs	https://doi.org/10.1109/ISIE45063.2020.9152544
Publication status	Published - 2020 Jun
Event	29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands Duration: 2020 Jun 17 → 2020 Jun 19

Publication series

Name	IEEE International Symposium on Industrial Electronics
Volume	2020-June

Conference

Conference	29th IEEE International Symposium on Industrial Electronics, ISIE 2020
Country/Territory	Netherlands
City	Delft
Period	20/6/17 → 20/6/19

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Control and Systems Engineering

UN SDGs

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

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10.1109/ISIE45063.2020.9152544

Cite this

Kim, S. J., Kim, S. H., Lee, H. M., Lim, S. H., Kwon, G. Y., & Shin, Y. J. (2020). State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length. In 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings (pp. 1336-1341). Article 9152544 (IEEE International Symposium on Industrial Electronics; Vol. 2020-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE45063.2020.9152544

Kim, Si Joong ; Kim, Seon Hyeog ; Lee, Hyeong Min et al. / State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length. 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1336-1341 (IEEE International Symposium on Industrial Electronics).

@inproceedings{29b4fc7728594670972ed39bef7e0fd1,

title = "State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length",

abstract = "This paper presents a lithium-ion battery state-of-health (SOH) estimation method based on a long short-term memory neural network. The proposed algorithm uses multi-input for the current state of the battery, which are voltage, current, temperature and state-of-charge (SOC). In addition, it reflects the historical state of the battery such as difference of current and temperature by varying the input sequence length, for a more accurate SOH estimation. To verify the proposed algorithm, the accuracy value when using the error function is checked. Then, the algorithm selects the appropriate sequence length and analyzes the system. The system improves estimation accuracy through the feedback process. Furthermore, the proposed method is compared to other learning methods to support the validation. The results highlight the selection of the appropriate sequence length can improve the accuracy of the estimation and show that the battery SOH estimation with the optimal sequence length is great effect on the performance through RMSE 0.1425, MAE 0.1084, and MAPE 0.8658%.",

keywords = "lithium-ion battery, long short-term memory (LSTM), sequence length, state of health (SOH)",

author = "Kim, {Si Joong} and Kim, {Seon Hyeog} and Lee, {Hyeong Min} and Lim, {Sue Hyang} and Kwon, {Gu Young} and Shin, {Yong June}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 29th IEEE International Symposium on Industrial Electronics, ISIE 2020 ; Conference date: 17-06-2020 Through 19-06-2020",

year = "2020",

month = jun,

doi = "10.1109/ISIE45063.2020.9152544",

language = "English",

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Kim, SJ, Kim, SH, Lee, HM, Lim, SH, Kwon, GY & Shin, YJ 2020, State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length. in 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings., 9152544, IEEE International Symposium on Industrial Electronics, vol. 2020-June, Institute of Electrical and Electronics Engineers Inc., pp. 1336-1341, 29th IEEE International Symposium on Industrial Electronics, ISIE 2020, Delft, Netherlands, 20/6/17. https://doi.org/10.1109/ISIE45063.2020.9152544

State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length. / Kim, Si Joong; Kim, Seon Hyeog; Lee, Hyeong Min et al.
2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1336-1341 9152544 (IEEE International Symposium on Industrial Electronics; Vol. 2020-June).

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

TY - GEN

T1 - State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length

AU - Kim, Si Joong

AU - Kim, Seon Hyeog

AU - Lee, Hyeong Min

AU - Lim, Sue Hyang

AU - Kwon, Gu Young

AU - Shin, Yong June

PY - 2020/6

Y1 - 2020/6

N2 - This paper presents a lithium-ion battery state-of-health (SOH) estimation method based on a long short-term memory neural network. The proposed algorithm uses multi-input for the current state of the battery, which are voltage, current, temperature and state-of-charge (SOC). In addition, it reflects the historical state of the battery such as difference of current and temperature by varying the input sequence length, for a more accurate SOH estimation. To verify the proposed algorithm, the accuracy value when using the error function is checked. Then, the algorithm selects the appropriate sequence length and analyzes the system. The system improves estimation accuracy through the feedback process. Furthermore, the proposed method is compared to other learning methods to support the validation. The results highlight the selection of the appropriate sequence length can improve the accuracy of the estimation and show that the battery SOH estimation with the optimal sequence length is great effect on the performance through RMSE 0.1425, MAE 0.1084, and MAPE 0.8658%.

AB - This paper presents a lithium-ion battery state-of-health (SOH) estimation method based on a long short-term memory neural network. The proposed algorithm uses multi-input for the current state of the battery, which are voltage, current, temperature and state-of-charge (SOC). In addition, it reflects the historical state of the battery such as difference of current and temperature by varying the input sequence length, for a more accurate SOH estimation. To verify the proposed algorithm, the accuracy value when using the error function is checked. Then, the algorithm selects the appropriate sequence length and analyzes the system. The system improves estimation accuracy through the feedback process. Furthermore, the proposed method is compared to other learning methods to support the validation. The results highlight the selection of the appropriate sequence length can improve the accuracy of the estimation and show that the battery SOH estimation with the optimal sequence length is great effect on the performance through RMSE 0.1425, MAE 0.1084, and MAPE 0.8658%.

KW - lithium-ion battery

KW - long short-term memory (LSTM)

KW - sequence length

KW - state of health (SOH)

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BT - 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 17 June 2020 through 19 June 2020

ER -

Kim SJ, Kim SH, Lee HM, Lim SH, Kwon GY, Shin YJ. State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length. In 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1336-1341. 9152544. (IEEE International Symposium on Industrial Electronics). doi: 10.1109/ISIE45063.2020.9152544

State of Health Estimation of Li-Ion Batteries Using Multi-Input LSTM with Optimal Sequence Length

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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