一种变参数模型平方根 UKF 锂离子电池SOC 估计方法

doi:10.12066/j.issn.1007-2861.2048

Abstract

Abstract:

The state of charge (SOC) estimation is an important aspect of modern battery management system. Methods based on Thevenin Equivalent model for Lithium Battery such as extended Kalman filter (EKF) have been widely used in SOC estimation. However, there are some limitations in the derivation of Jacobian matrix and linearization. In this paper, a square root unscented Kalman filter (SRUKF) based on variable-parameter model is proposed to estimate SOC, which does not need to linearize the nonlinear model. At the same time, the square root property improves the numerical properties of the state covariance. The variable-parameter model is based on the second order Thevenin model, which makes the parameters of the lithium battery change with the quantity of electricity and reduces the error caused by the fixed parameter model that cannot reflect the change of parameters under different electric quantity. The experiment results show that the proposed method is effective. Compared with the existing SOC estimation method, the EKF, UKF and the estimation results from the fixed-parameter model, the error of this method is obviously smaller than that of the other three methods.

Key words: Li-ion battery, state of charge, variable-parameter model, square root unscented Kalman filter (UKF)

CLC Number:

TM912

GAO Wenkai, YAN Limin, SUN Die. A square root UKF with variable-parameter model for Li-ion batteries SOC estimation method[J]. Journal of Shanghai University（Natural Science Edition）, 2020, 26(3): 413-424.

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A square root UKF with variable-parameter model for Li-ion batteries SOC estimation method

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[1]	JI Xiang1, SONG Yi-cheng2,3, ZHANG Jun-qian2,3. Molecular Dynamics Simulation of Diffusion Properties of Li_xC₆ in Li-Ion Battery [J]. Journal of Shanghai University（Natural Science Edition）, 2014, 20(1): 68-74.
[2]	LI Zhi-hua, ZHANG Qing-chun, LIU Zhen. Fuzzy Estimation of Residual Capacity for Lead-Acid Batteries [J]. Journal of Shanghai University（Natural Science Edition）, 2009, 15(4): 364-368.