Estimation of SOC of lithium batteries based on IFA-EKF

doi:10.12028/j.issn.2095-4239.2019.0127

Abstract

Abstract:

As the key technology associated with the battery management systems of electric vehicles, the state of charge (SOC) of lithium-ion batteries describes the residual capacity and indicates the remaining mileage of electric vehicles. An extended Kalman filter (EKF), which is optimized by the improved Firefly algorithm, is proposed to research the estimation of the SOC of lithium-ion batteries for electric vehicles. The state-space representation of the battery model is estimated based on the second-order resistor-capacitor (RC) equivalent circuit model, which uses a pulse power characteristic test experiment to rapidly estimate the model parameters. Subsequently, the Firefly algorithm is applied to optimize the covariance of the system noise matrix and measurement matrix in the EKF to improve the SOC estimation accuracy. After performing the simulation experiments under dynamic and static conditions, the results denote that an algorithm for the estimation of SOC based on IFA–EKF results in a lower absolute maximum error and average absolute error when compared with those obtained via the EKF algorithm. Furthermore, the proposed algorithm offers improved accuracy and practicality.

Key words: lithium battery, charged state, firefly algorithm, extended Kalman filter

CLC Number:

TM 911

ZHANG Yuanjin, WU Huawei, YE Congjin. Estimation of SOC of lithium batteries based on IFA-EKF[J]. Energy Storage Science and Technology, 2020, 9(1): 117-123.

Figures/Tables 14

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放电实验V₀与SOC关系数据		充电实验V₀与SOC关系数据		V₀与SOC最终关系数据
SOC	V₀	SOC	V₀	SOC	V₀
1	4.170	1	4.172	1	4.1710
0.9	4.041	0.9	4.047	0.9	4.0440
0.8	3.936	0.8	3.946	0.8	3.9410
0.7	3.837	0.7	3.843	0.7	3.8400
0.6	3.750	0.6	3.754	0.6	3.7520
0.5	3.681	0.5	3.690	0.5	3.6855
0.4	3.640	0.4	3.645	0.4	3.6425
0.3	3.607	0.3	3.610	0.3	3.6085
0.2	3.573	0.2	3.581	0.2	3.5770
0.1	3.520	0.1	3.534	0.1	3.5270
0	3.448	0	3.459	0	3.4535

SOC	R	R₁	C₁	R₂	C₂
1	21.03	4.57	609.08	35.23	1642.68
0.9	28.15	4.89	573.32	30.28	1656.93
0.8	27.23	5.12	569.65	34.29	1497.83
0.7	27.49	4.98	600.03	36.84	1379.73
0.6	25.68	4.34	667.21	38.34	1168.32
0.5	28.48	3.08	879.27	21.45	1617.29
0.4	29.71	3.29	1143.57	22.31	2053.23
0.3	30.08	2.79	639.45	27.37	1675.20
0.2	30.96	4.48	302.34	29.48	1521.23
0.1	35.70	12.17	32.45	32.23	1184.23

算法	SOC平均误差绝对值	SOC最大误差绝对值
IFA-EKF	0.0185	0.0544
EKF	0.0332	0.0607

算法	SOC平均误差绝对值	SOC最大误差绝对值
IFA-EKF	0.0246	0.0713
EKF	0.0407	0.0900

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