SOC estimation of Li-ion batteries based on Gaussian process regression and UKF

doi:10.19799/j.cnki.2095-4239.2020.0003

Abstract

Abstract:

The high-precision state-of-charge (SOC) estimation of battery power capacity is the key technology associated with a battery management system, and its estimation accuracy directly influences the energy management efficiency and endurance mileage of electric vehicles. The traditional filter estimation method uses an estimation model and does not consider the accuracy model of a Li-ion battery. To solve this problem, an unscented Kalman filter (UKF) estimation method based on Gaussian process regression (GPR) is presented. GPR can be used to establish a measurement equation for an equivalent circuit model with limited training data, resulting in the connection of UKF and GPR. The proposed model optimally uses the data obtained via the tests and the current to estimate the SOC. The experimental results and comparative analysis of the UKF estimation method based on Gaussian process regression demonstrate the high prediction accuracy of the proposed algorithm during SOC estimation.

Key words: power battery, state of charge, Gaussian process regression, UKF

CLC Number:

TM 911

WEI Meng, LI Jiabo, LI Zhongyu, YE Min, XU Xinxin. SOC estimation of Li-ion batteries based on Gaussian process regression and UKF[J]. Energy Storage Science and Technology, 2020, 9(4): 1206-1213.

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SOC/%	R₀	R₁	R₂	C₁	C₂
10	0.0249	0.0189	0.0038	481.13	27.797
20	0.0217	0.0142	0.0067	637.41	21.198
30	0.0249	0.0126	0.0037	610.7	17.04
40	0.0243	0.0119	0.0046	629.43	16.709
50	0.0246	0.0146	0.0051	741.15	17.825
60	0.0237	0.0125	0.0055	622.14	14.426
70	0.0263	0.0139	0.0043	600.09	13.872
80	0.0246	0.0152	0.0043	438.51	15.042
90	0.028	0.016	0.0043	516.05	28.705
100	0.263	0.0161	0.0043	477.78	28.705

方法	RMSE/%	MAPE/%
UKF (UDDS)	1.93	2.50
GPR-UKF (UDDS)	1.57	2.24
UKF (DST)	1.81	3.73
GPR-UKF (DST)	1.48	3.52

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