State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries

Hanqing Yu, Lisheng Zhang, Wentao Wang, Shen Li, Siyan Chen, Shichun Yang, Junfu Li and Xinhua Liu

Energy, 2023, vol. 278, issue C

Abstract: To ensure the secure and healthy usage of lithium-ion batteries, it is necessary to accurately estimate the state of charge (SOC) in battery management systems. The development of deep learning (DL) provides a new solution for battery SOC estimation. However, the directly measured physical quantities contain less useful information and have low estimation accuracy. This paper proposes a method of integrating the mechanism knowledge of the battery domain into the DL framework. Firstly, the simplified electrochemical model is utilized to obtain the mechanism-related physical variables to expand the input of the DL model. Secondly, the long short-term memory (LSTM) network is used with the Bayesian optimization, and the variables with high correlation are identified. The best SOC estimation performance can be obtained by adding all the selected highly-correlated variables to the input for training together. The results show that the proposed method can improve the SOC estimation performance with only a slight increase in computation cost. Finally, other DL models are utilized to further validate the effectiveness, to reveal the universality. These results show that the performance of the DL model can be effectively improved by using the knowledge of the battery domain.

Keywords: Lithium-ion battery; State of charge; Deep learning; Electrochemical model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223012409
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:278:y:2023:i:c:s0360544223012409

DOI: 10.1016/j.energy.2023.127846

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().