Design of a Non-Linear Observer for SOC of Lithium-Ion Battery Based on Neural Network

Ning Chen, Xu Zhao, Jiayao Chen, Xiaodong Xu, Peng Zhang and Weihua Gui
Additional contact information
Ning Chen: School of Automation, Central South University, Changsha 410083, China
Xu Zhao: School of Automation, Central South University, Changsha 410083, China
Jiayao Chen: School of Automation, Central South University, Changsha 410083, China
Xiaodong Xu: School of Automation, Central South University, Changsha 410083, China
Peng Zhang: School of Automation, Central South University, Changsha 410083, China
Weihua Gui: School of Automation, Central South University, Changsha 410083, China

Energies, 2022, vol. 15, issue 10, 1-26

Abstract: This paper presents a method for use in estimating the state of charge (SOC) of lithium-ion batteries which is based on an electrochemical impedance equivalent circuit model with a controlled source. Considering that the open-circuit voltage of a battery varies with the SOC, an equivalent circuit model with a controlled source is proposed which the voltage source and current source interact with each other. On this basis, the radial basis function (RBF) neural network is adopted to estimate the uncertainty in the battery model online, and a non-linear observer based on the radial basis function of the RBF neural network is designed to estimate the SOC of batteries. It is proved that the SOC estimation error is ultimately bounded by Lyapunov stability analysis, and the error bound can be arbitrarily small. The high accuracy and validity of the non-linear observer based on the RBF neural network in SOC estimation are verified with experimental simulation results. The SOC estimation results of the extended Kalman filter (EKF) are compared with the proposed method. It improves convergence speed and accuracy.

Keywords: lithium-ion batteries; state of charge; fraction order; electrochemical impedance model; non-linear observer based on RBF neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/10/3835/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/10/3835/ (text/html)

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:gam:jeners:v:15:y:2022:i:10:p:3835-:d:821996

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().