Novel multiple weighted-AEKF method for online state-of-charge estimation of lithium-ion batteries

doi:10.19799/j.cnki.2095-4239.2021.0242

Abstract

Abstract:

To accurately estimate the state-of-charge of high power lithium-ion battery in real-time, the ternary lithium-ion battery is used as the research object and a novel multiple weighted-adaptive extended Kalman filtering method is proposed. The estimation value is corrected multiple times using the residual and Kalman gain, and different weights are configured for each residual based on the amount of information contained. The current estimated value is adjusted and corrected ad-hoc based on the real-time update of system noise covariance and error covariance. To test the algorithm's logic, the second-order RC equivalent circuit model is used to characterize the dynamic characteristics of the battery, and experimental verification is performed under various working conditions. The experimental results show that the estimation root-mean-square-error under HPPC, DST, and BBDST working conditions is 1.31% and 1.23%, respectively, demonstrating the proposed algorithm's good accuracy and convergence. The novel multiple weighted-adaptive extended Kalman filtering method lays the theoretical groundwork for accurate state estimation and widespread application of lithium-ion batteries.

Key words: lithium-ion battery, second-order RC model, state-of-charge, multiple weighted, adaptive extended Kalman filtering

CLC Number:

TM 912

Jialu QIAO, Shunli WANG, Chunmei YU, Weihao SHI, Xiao YANG. Novel multiple weighted-AEKF method for online state-of-charge estimation of lithium-ion batteries[J]. Energy Storage Science and Technology, 2021, 10(6): 2318-2325.

Figures/Tables 11

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对比方式	EKF	AEKF	加权多新息AEKF
MAE	1.24%	1.20%	0.92%
RMSE	1.53%	1.47%	1.31%

对比方式	EKF	AEKF	加权多新息AEKF
MAE	2.45%	1.53%	0.97%
RMSE	2.71%	1.70%	1.23%

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