State of Charge Estimation for Lithium-ion Battery using Recurrent Neural Network

Van-Tsai Liu, Yikai Sun, Hong-yi Lu, Sun-Kai Wang
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引用次数: 5

Abstract

In this paper combines the artificial neural network (ANN) method and the internal resistance measuring method, which is designed for the lithium-ion battery state of charge (SOC) estimation. It is different from the general neural network research that only uses voltage and current as parameters. The input layer adds important parameters: the battery voltage and current, and the internal resistance of the battery as external inputs. We design a recurrent neural networks (RNN) model with non-linear autoregressive with exogenous input (NARX). The network compares the difference between the simulation results under the same benchmark with the back-propagation neural networks (BPNN). Experiments show that this architecture not only improves the convergence speed of the neural network, but also shortens its average execution time, and the mean-square error is improved. It is a good indicator of the accuracy of the measurement. This paper also discusses the application of this architecture to the difference between DC internal resistance and AC internal resistance.
基于递归神经网络的锂离子电池充电状态估计
本文将人工神经网络(ANN)方法与内阻测量方法相结合,设计了一种用于锂离子电池荷电状态(SOC)估计的方法。它不同于一般的神经网络研究只使用电压和电流作为参数。输入层增加了重要的参数:电池电压和电流,以及作为外部输入的电池内阻。我们设计了一个带有外生输入的非线性自回归的递归神经网络(RNN)模型。该网络比较了相同基准下的仿真结果与反向传播神经网络(BPNN)的差异。实验表明,该结构不仅提高了神经网络的收敛速度,而且缩短了神经网络的平均执行时间,均方误差也得到了改善。这是测量精度的一个很好的指标。本文还讨论了该结构在直流内阻和交流内阻差中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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