A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery

Lin He, Xingwen Hu, Guangwei Yin, Guoqiang Wang, Xingguo Shao and Jichao Liu

Energy, 2024, vol. 288, issue C

Abstract: Compared with the battery voltage error, in this article, the battery current error between the predicted current and the measured, is used as the input of the observer to estimate the state-of-charge. The current-based observer for the state-of-charge estimation requires a current dynamics model to formulate the lithium-ion battery, making its differential equation contain a load-like variation of the state-of-charge more importantly. Combining the current differential equation with the equivalent circuit model for the lithium-ion battery, a novel current dynamics model is formulated and utilized to predict the battery current. Then, a proportional-integral observer is designed to estimate the state-of-charge by the battery current error, and both the battery model parameters and the battery nominal capacity are updated in real time. The current-based proportional-integral observer algorithm is downloaded into a battery management system and tested in a battery electric vehicle. Some comparative experiments are carried out among the current-based observer, the current-integral method, and the extended Kalman filter. According to the experimental results and the statistical analyses, it is shown that the proportional-integral observer based on the current dynamics model is a good candidate for the accurate estimation of the state-of-charge.

Keywords: Battery current error; Equivalent circuit model; Battery model parameters; Battery nominal capacity; Extended Kalman filter (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223030955
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:288:y:2024:i:c:s0360544223030955

DOI: 10.1016/j.energy.2023.129701

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 ().