SOC estimation of lithium battery based on IBAS-NARX neural network model

doi:10.19799/j.cnki.2095-4239.2021.0291

Abstract

Abstract:

When using neural network methods to estimate the state of charge of lithium batteries, the traditional state of charge fitness evaluation function has the disadvantage of only considering the network weight parameters such as the mean square error and ignoring the influence of topology parameters on the model. Therefore, this paper proposes to consider the weighted influence of model topology parameters and network weight parameters, such as input/output timing correlation, and the number of hidden layer neurons, in the design of the fitness evaluation function and introduce it into a nonlinear autoregressive neural network with external input. Based on the improved longhorn whisker search algorithm, the estimation of the state of charge of the lithium battery in the modeling method involves the collaborative identification and optimization of the above-mentioned model topology parameters and network weight parameters. The simulation results show that the method proposed in this paper can improve the accuracy of estimating the state of charge of a lithium battery under a variety of complex working conditions. The root mean square error of the lithium battery state of charge under DST standard working condition and WLTC standard working condition is 3.38×10^-3 and 8.75×10^-4, respectively, which improves the estimation accuracy of the root mean square error by 42.4% and 20.5% when compared to the BAS-NARX neural network.

Key words: lithium battery, SOC estimation, beetle antennae search algorithm, NARX neural network

CLC Number:

TM 912

Xinyu CAO, Fei PENG, Liwei LI, Jianguang YIN. SOC estimation of lithium battery based on IBAS-NARX neural network model[J]. Energy Storage Science and Technology, 2021, 10(6): 2342-2351.

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References 17

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算法	均方根误差(RMSE)	最大误差
BAS-BP神经网络	2.01×10^-2	8.25%
BAS-NARX神经网络	5.87×10^-3	1.65%
IBAS-NARX神经网络	3.38×10^-3	0.76%

算法	均方根误差(RMSE)	最大误差
BAS-BP神经网络	3.70×10^-3	1.72%
BAS-NARX神经网络	1.12×10^-3	0.47%
IBAS-NARX神经网络	8.75×10^-4	0.31%

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