State of charge estimation of Li-ion battery based on adaptive extended Kalman filter

doi:10.19799/j.cnki.2095-4239.2020.0071

Abstract

Abstract:

The state of charge (SOC) of the Li-ion battery is an important parameter associated with a battery management system. However, when estimating the SOC, external factors, such as the accuracy of the measuring equipment and noise, can interfere and reduce the SOC estimation accuracy. In this study, an adaptive extended Kalman filter (EKF) is proposed to improve the estimation accuracy and stability of SOC. Compared with traditional EKF, the estimation error of our method can be controlled within 3%, demonstrating the validity of the proposed model.

Key words: lithium-ion battery, BMS, SOC, extended Kalman filter

CLC Number:

TM 911

LI Jiabo, WEI Meng, LI Zhongyu, JIAO Shengjie, YE Min, XU Xinxin. State of charge estimation of Li-ion battery based on adaptive extended Kalman filter[J]. Energy Storage Science and Technology, 2020, 9(4): 1147-1152.

Figures/Tables 9

Fig.1

Table 1

Fig.2

Table 2

Online parameter identification based on RLS"

RLS参数辨识流程

Step1 初始化 $θ k$ ， $φ k$ 和 $P k$

Step2 计算k时刻的最小二乘估计

$θ ? (k) = θ ? k - 1 + K k [y k - φ T k θ ? k - 1]$

Step 3 计算估计误差：

$e k = y k - φ T k θ ? (k - 1)$

Step 4 计算增益矩阵：

$K k = P k - 1 φ k 1 + φ T k P k - 1 φ k$

Step 5 计算协方差矩阵：

$P k = [I - K k φ T k] P k - 1$

Step 6 重复Step2～Step5，之后跳出循环。

Table 2

Table 3

EKF algorithm flowchart"

EKF步骤：

Step1 初始化滤波值：x₀，P₀，Q₀，R₀；

Step2 更新状态变量： $x ? k = f (x k - 1, u k - 1)$ ；

Step 3 误差协方差时间更新： $P k = A k P k - 1 A k T + Q k - 1$ ；

Step 4 误差更新： $E k = y k - h (x ? k, u k)$ ；

Step 5 计算卡尔曼增益： $K k = P k - C k T (C k P k - C k T + R k - 1) - 1$ ；

Step 6 状态变量更新： $x ? k + 1 = x ? k + K k E k$ ；

Step 7 误差协方差测量更新： $P k + 1 = (I - K k C k) P k$ ；

Step 8 重复以上步骤，直至跳出循环。

Table 3

Fig.3

Fig.4

Fig.5

Fig.6

References 10

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