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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1336-1341
Number of pages6
ISBN (Electronic)9781728156354
DOIs
Publication statusPublished - 2020 Jun
Event29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands
Duration: 2020 Jun 172020 Jun 19

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2020-June

Conference

Conference29th IEEE International Symposium on Industrial Electronics, ISIE 2020
CountryNetherlands
CityDelft
Period20/6/1720/6/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

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