State of Charge Estimation of Lithium-Ion Batteries Using a Discrete-Time Nonlinear Observer

In this paper, a novel approach using the discrete-time nonlinear observer (DNLO) is proposed for state-of-charge (SOC) estimation of lithium-ion batteries (LIBs). To design the DNLO for SOC estimation, the state equations based on a second-order resistor-capacitor equivalent circuit model are deriv...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2017-11, Vol.64 (11), p.8557-8565
Main Authors: Li, Weilin, Liang, Liliuyuan, Liu, Wenjie, Wu, Xiaohua
Format: Article
Language:English
Subjects:
Batteries
Computer simulation
Convergence
Discharges (electric)
Discrete-time nonlinear observer (DNLO)
Electronic countermeasures
Equations of state
equivalent circuit model (ECM)
Equivalent circuits
Extended Kalman filter
Integrated circuit modeling
Lithium
Lithium-ion batteries
lithium-ion battery (LIB)
Lyapunov stability
Mathematical models
Observers
Open circuit voltage
Order parameters
Parameter identification
Polynomials
Rechargeable batteries
State of charge
state of charge (SOC)
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Summary:In this paper, a novel approach using the discrete-time nonlinear observer (DNLO) is proposed for state-of-charge (SOC) estimation of lithium-ion batteries (LIBs). To design the DNLO for SOC estimation, the state equations based on a second-order resistor-capacitor equivalent circuit model are derived to simulate the dynamic behaviors of an LIB. Considering the hysteresis characteristic of the battery, the model parameters depend on the SOC and the direction of battery current simultaneously, and then, the exponential-function fitting method is adopted to identify the offline results of the parameters. The ninth-order polynomial function is adopted to represent the highly nonlinear relationship between the open-circuit voltage and the SOC. The Lyapunov stability theory is used to prove the convergence of the proposed DNLO. The performance of the proposed method is also verified by the experiments based on the hybrid pulse power characteristic test, which indicates that compared with the extended Kalman filter (EKF) and the discrete-time sliding mode observer (SMO) algorithms, the proposed observer has better performance in reducing the computation cost, improving the estimation accuracy and enhancing the convergence capability.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2703685