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Fractional Order Terminal Sliding Mode Observer for State of Charge Estimation of Lithium-Ion Battery

Ma, Yunchen, Xu, Dezhi, Yang, Weilin, Pan, Tinglong, Ding, Yueheng (2023) Fractional Order Terminal Sliding Mode Observer for State of Charge Estimation of Lithium-Ion Battery. In: 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS). 12th Data Driven Control and Learning Systems Conference (DDCLS). . IEEE (doi:10.1109/ddcls58216.2023.10167117) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:102097)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://doi.org/10.1109/ddcls58216.2023.10167117

Abstract

Lithium-ion battery is widely used for its high energy density, long cycle life, non-pollution and other advantages. They are expected to be mainstream power source for future applications on electrochemical energy storage. However, it has been a long-standing problem to obtain an accurate real-time state estimation for lithium-ion battery with high nonlinearity and inevitable inconsistency. In this paper, the design of a new estimation method for state of charge (SOC) of lithium-ion battery is introduced. Based on the equivalent Thevenin model, a fractional order sliding mode observer is proposed to estimate terminal voltage, SOC and polarization voltage and the stability proof is given. Compared to the frequently-used Kalman filter method and the current integration method, this method shows higher accuracy and robustness. As a result, the feasibility of the proposed method is verified.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/ddcls58216.2023.10167117
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: National Natural Science Foundation of China (https://ror.org/01h0zpd94)
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 19 Jul 2023 10:53 UTC
Last Modified: 19 Jul 2023 10:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/102097 (The current URI for this page, for reference purposes)

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