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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings of IncoME-VI and TEPEN 2021
Subtitle of host publicationPerformance Engineering and Maintenance Engineering
EditorsHao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
PublisherSpringer, Cham
Pages707-719
Number of pages13
Volume117
ISBN (Electronic)9783030990756
ISBN (Print)9783030990749
DOIs
Publication statusPublished - 18 Sep 2022
Event6th 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 202123 Oct 2021
Conference number: 6
https://tepen.net/conference/tepen2021/

Publication series

NameMechanisms and Machine Science
PublisherSpringer
Volume117
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

Conference6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Abbreviated titleTEPEN-2021 and IncoME-VI
Country/TerritoryChina
CityTianjin
Period20/10/2123/10/21
Internet address

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