基于加权多新息AEKF的锂电池SOC在线估算

doi:10.19799/j.cnki.2095-4239.2021.0242

摘要/Abstract

摘要：

为实现大功率锂离子电池荷电状态的实时准确估算，以三元锂离子电池为研究对象，提出了一种加权多新息理论与自适应扩展卡尔曼滤波相结合的算法。利用多个时刻的残差和卡尔曼增益对估计值进行校正，并根据所包含的信息量为每个残差配置不同的权重。通过对系统噪声协方差和误差协方差的实时更新，自适应地调节和修正当前估计值。为验证算法合理性，采用二阶RC等效电路模型来表征电池动态特性，并在不同工况下进行实验验证。实验结果表明，在DST和BBDST工况下的估算均方根误差分别为1.31%和1.23%，验证了所提出算法具有良好的精度和收敛性。加权多新息自适应扩展卡尔曼滤波算法为锂电池的精确状态估算和广泛应用提供了理论基础。

关键词: 锂离子电池, 二阶RC模型, 荷电状态, 加权多新息, 自适应扩展卡尔曼滤波

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

中图分类号:

TM 912

乔家璐, 王顺利, 于春梅, 史卫豪, 杨潇. 基于加权多新息AEKF的锂电池SOC在线估算[J]. 储能科学与技术, 2021, 10(6): 2318-2325.

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.

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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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