Estimation of the SOC of a battery based on the AUKF-BP algorithm

doi:10.19799/j.cnki.2095-4239.2020.0285

Abstract

Abstract:

The estimation of the battery state of charge (SOC) is a core feature of the on-board battery management system (BMS). Its accurate estimation can prolong the service life of a battery and ensure the normal driving of a vehicle. Using lithium-ion batteries as the model, this paper proposes a battery SOC estimation method based on the combination of the adaptive unscented Kalman filter (AUKF) and the BP neural network. This method improves the estimation accuracy of UKF through adaptive sampling and uses the SOC output value of the trained BP neural network for the observation of UKF. Based on the battery test data under mixed working conditions and the FUDS working conditions collected by the Arbin battery test platform at varied temperatures (0 ℃, 25 ℃, and 40 ℃), the accuracy of the AUKF-BP algorithm versus the BP algorithm were evaluated. The results indicate that the average mean error of the AUKF-BP algorithm at different temperatures was 0.82%, and the average mean error of the BP algorithm was 1.63%. Overall, an SOC estimation method based on the AUKF-BP algorithm is the most accurate.

Key words: lithium ion battery, SOC estimation, BP neural network, AUKF

CLC Number:

TM 912.6

Yuanjin ZHANG, Huawei WU, Congjin YE. Estimation of the SOC of a battery based on the AUKF-BP algorithm[J]. Energy Storage Science and Technology, 2021, 10(1): 237-241.

Figures/Tables 7

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Table 1

References 14

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环境温度/℃	最大误差/%		最小误差/%		均值误差/%
环境温度/℃	BP	AUKF-BP	BP	AUKF-BP	BP	AUKF-BP
-10	4.10	2.87	0.03	0.02	1.60	0.69
0	6.72	5.87	0.05	0	1.38	1.18
10	5.40	2.88	0.06	0.01	1.97	0.88
25	5.11	2.06	0.01	0	1.65	0.62
40	4.81	2.71	0.02	0.01	1.56	0.75

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