Estimation of state of charge of battery based on improved multi-innovation extended Kalman filter

被引：0

作者：

Lei K.-B. ^{[1
]}

Chen Z.-Q. ^{[1
]}

机构：

[1] State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai

来源：

Chen, Zi-Qiang (chenziqiang@sjtu.edu.cn) | 1978年 / Zhejiang University卷 / 55期

关键词：

Estimation of SOC; Hardware in loop; Kalman filter; Lithium-ion battery; Multi-innovation recognition;

D O I：

10.3785/j.issn.1008-973X.2021.10.020

中图分类号：

学科分类号：

摘要：

An improved multi-innovation extended Kalman filter was proposed based on the forgetting factor in order to improve the accuracy of SOC estimation. Dual-polarization equivalent circuit model of lithium-ion battery was established, and open-circuit voltage testing was conducted. Recursive least squares method was used to realize online battery model parameter identification. FMIEKF was proposed for SOC estimation based on the fusion of multi-innovation identification theory and Kalman filtering. A forgetting factor was introduced to weaken the historical data correction weight and solve the problem of data oversaturation. The method was verified through experiments and hardware-in-the-loop. The experimental results show that FMIEKF has higher accuracy and convergence. The maximum estimation error was 0.948%, the average error was 0.214%, and the FMIEKF converged within 20 seconds under different initial values of SOC. The method can be applied to the actual battery management system. © 2021, Zhejiang University Press. All right reserved.

引用

页码：1978 / 1985and2001

共 16 条

[1]

GRUOSSO G, GAJANI G, RUIZ F, Et al., A virtual sensor for electric vehicles' state of charge estimation, Electronics, 9, 2, (2020)

[2]

LI Y, CHATTOPADHYAY P, XIONG S, Et al., Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge, Applied Energy, 184, 12, pp. 266-275, (2016)

[3]

ZHOU Shi-yao, CHEN Zi-qiang, ZHENG Chang-wen, Et al., Electrical characteristics of power lithium-ion batteries used in all-sea deep submersibles, Journal of Shanghai Jiaotong University, 53, 1, pp. 49-54, (2019)

[4]

HAN Xue-bing, Research on mechanism model and state estimation of lithium-ion batteries for vehicles, (2014)

[5]

ZHENG C, CHEN Z, HUANG D., Fault diagnosis of voltage sensor and current sensor for lithium-ion battery pack using hybrid system modeling and unscented particle filter, Energy, 191, 1, (2020)

[6]

LIU Zheng-yu, YANG Jun-bin, ZHANG Qing, Et al., Lithium battery SOC prediction based on QPSO-BP neural network, Journal of Electronic Measurement and Instrument, 27, 3, pp. 44-48, (2013)

[7]

KLASS V, BEHM M, LINDBERGH G., Capturing lithium-ion battery dynamics with support vector machine-based battery model, Journal of Power Sources, 298, 8, pp. 92-101, (2015)

[8]

PING S, OUYANG M, LU L, Et al., The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles, IEEE Transactions on Vehicular Technology, 67, 1, pp. 92-103, (2018)

[9]

XIONG R, GONG X, MI C C, Et al., A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter, Journal of Power Sources, 243, 6, pp. 805-816, (2013)

[10]

DUAN J, WANG P, MA W, Et al., State of charge estimation of lithium battery based on improved correntropy extended Kalman filter, Energies, 13, 16, (2020)

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