State estimation of lithium-ion battery based on dual adaptive Kalman filter

doi:10.19799/j.cnki.2095-4239.2021.0411

Abstract

Abstract:

An accurate lithium-ion battery model is very important to ensure the reliability of the battery. Accurate estimation of state of charge (SOC) ensures the safety and efficient operation of specific applications. To improve the estimation accuracy of SOC, an equivalent circuit model is established and the parameters are identified using bias compensation recursive least squares (BCRLS) of the forgetting factor. The SOC is then estimated using the adaptive unscented Kalman filter (AUKF) algorithm. The AUKF algorithm defined by weight vectors was proposed to improve the estimation accuracy of SOC because of the vulnerability of the unscented Kalman filter technique to nonlinear variables. However, the internal characteristics of the battery will change during the discharge process, and the ohmic internal resistance of the battery will have a direct effect on the SOC estimations. Based on this, we propose a dual AUKF to further improve the estimation accuracy of SOC. Compared with other algorithms, the experimental results show that the proposed algorithm's error in estimating SOC is less than 2%, demonstrating the effectiveness of the algorithm.

Key words: lithium ion battery, state of charge (SOC), bias compensation recursive least squares, weight vectors, dual adaptive unscented Kalman filter

CLC Number:

TM 911

Pengchao HUANG, Jiaqiang E. State estimation of lithium-ion battery based on dual adaptive Kalman filter[J]. Energy Storage Science and Technology, 2022, 11(2): 660-666.

Figures/Tables 6

Fig. 1

Table 1

Parameter identification process based on BCRLS"

算法辨识流程如下：
步骤1计算RLS方法的的增益矩阵
$K (t) = P (t - 1) φ (t) 1 + φ T (t) P (t - 1) φ (t)$	(6)
步骤2计算迭代新息
$e (t) = y (t) - φ T (t) θ L S (t - 1)$	(7)
步骤3参数估计
$θ L S (t) = θ L S (t - 1) + K (t) e (t)$	(8)
步骤4计算相应的误差准则值
$J (t) = J (t - 1) + e 2 (t) 1 + φ T (t) P (t - 1) φ (t)$	(9)
步骤5更新噪声协方差矩阵
$P (t) = [I - K (t) φ T (t)] P (t - 1)$	(10)
步骤6参数补偿后的参数矩阵为
$θ (t) = θ L S (t) + K (t) σ 2 (t) P (t) θ (t - 1)$	(11)
步骤7更新噪声协方差矩阵
$σ 2 (t) = J (t) k 1 + θ L S T (t - 1) θ L S (t - 1)$	(12)
步骤8重复步骤1～步骤7，完成最终的辨识参数。

Table 1

Fig. 2

Fig. 3

Fig. 4

Table 2

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模型	SOC误差		端电压误差(MAE)/%V
模型	MAE/%	RMSE/%	端电压误差(MAE)/%V
FFRLS-AUKF	3.11	2.17	4.12
BCRLS-AUKF	2.89	2.08	3.23
BCRLS-改进的AUKF	2.42	1.89	2.94
所提模型	1.45	1.52	2.23

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