用于电荷状态估计的变分模式分解时间卷积网络模型

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhaocheng Zhang, Tao Cai, Aote Yuan
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引用次数: 0

摘要

由于电动汽车(EV)的快速增长,电池充电状态(SOC)的估计受到了极大的研究兴趣。原因是准确的SOC估计可以显著提高电动汽车的可靠性。提出了一种基于变分模式分解(VMD)技术的时间卷积网络(TCN)SOC估计模型。该方法首先采用时频分析技术将电压值分解到不同的频域,并使用VMD技术对每个频域进行分析,以获得其特征作为TCN模型的输入。然后,所提出的方法将不同频域的输出与注意力模块相结合,作为TCN模型的最终输出。在实际电池数据集上的实验表明,该方法的平均绝对误差和均方根误差分别比现有方法高7.2%和6.13%。此外,使用所提出的方法的估计值和实际值之间的误差在2%以内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Variational mode decomposition enabled temporal convolutional network model for state of charge estimation

Variational mode decomposition enabled temporal convolutional network model for state of charge estimation

Due to the fast growth of electric vehicles (EVs) , estimation for Battery's State-of-charge (SOC) received significant research interests. The reason is that an accurate SOC estimation can significantly contribute to the reliability of EVs. A Variational Mode Decomposition (VMD) technique enabled Temporal Convolutional Network (TCN) model is proposed by the authors for SOC estimation. The proposed method first adopts time-frequency analysis techniques to decompose voltage values into different frequency domains, each of which is analysed with the VMD technique to obtain its features as the input for the TCN model. Then, the proposed method combines outputs of different frequency domains with an attention module as the final output of the TCN model. Experiments on real battery datasets indicate that the proposed method outperforms the existing methods by 7.2% in mean absolute error and 6.13% in root mean square error. In addition, the error between the estimated and actual values using the proposed method is bounded by 2%.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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