State of charge estimation of Li-ion battery based on BCRLS-ACKF

doi:10.19799/j.cnki.2095-4239.2021.0205

Abstract

Abstract:

Accurate estimation of the state of charge (SOC) of Li-ion batteries is essential for the battery management system. Model parameter identification is the premise of SOC estimation and the key factor affecting its estimation accuracy. Bias compensation recursive least squares (BCRLS) is used for online parameter identification to effectively avoid noise's influence on parameter identification. The adaptive cubature Kalman filter (ACKF) algorithm is used to estimate the battery SOC on this basis, and the system noise is updated in real-time to improve estimation accuracy. In addition, for the problem that the square root cannot be decomposed due to the loss of positive definiteness of the covariance matrix in the calculation process, the singular value decomposition method is used instead of Cholesky decomposition to improve the stability of numerical calculation. Finally, BCRLS and ACKF are combined to realize joint estimation of model parameters and SOC, and the algorithm is validated under various working conditions and with incorrect initial values. The results show that the algorithm proposed in this paper has high accuracy, and the average absolute error is within 2%.

Key words: state of charge, BCRLS, singular value decomposition, ACKF, joint estimation

CLC Number:

TM 912

Hang SU, Huaibin GAO, Zhengguang LI, Hongjun LI, Jianfei LIU, Xiaobo ZUO, Linlin JI. State of charge estimation of Li-ion battery based on BCRLS-ACKF[J]. Energy Storage Science and Technology, 2021, 10(6): 2334-2341.

Figures/Tables 10

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SOC/%		R₀/Ω	R₁/Ω	R₂/Ω	C₁/F	C₂/F
FFRLS	20	0.096	0.035	0.077	808.83	3314.91
	40	0.075	0.022	0.063	656.97	3812.05
	60	0.079	0.027	0.091	731.45	4286.24
	80	0.074	0.024	0.078	583.35	4036.52
BCRLS	20	0.099	0.044	0.065	721.11	4264.37
	40	0.086	0.037	0.079	746.46	3578.78
	60	0.077	0.034	0.072	628.67	3261.44
	80	0.083	0.041	0.089	533.89	3835.53

算法	Max-AE/%	MAE/%	RMSE/%
FFRLS-CKF	4.87	3.19	3.21
BCRLS-CKF	3.66	2.29	2.31
BCRLS-AUKF	3.63	1.16	1.49
BCRLS-ACKF	3.49	0.65	0.85

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