Fractional-Order GRU Networks With Memory Units Based on Hausdorff Difference for SOC Estimations of Lithium-Ion Batteries

被引:0
|
作者
Gao, Xue [1 ]
Jia, Kai [1 ]
Gao, Zhe [1 ,2 ]
Xiao, Shasha [1 ]
机构
[1] Liaoning Univ, Sch Math & Stat, Shenyang 110036, Peoples R China
[2] Liaoning Univ, Coll Light Ind, Shenyang 110036, Peoples R China
关键词
State of charge; Estimation; Logic gates; Accuracy; Mathematical models; Lithium-ion batteries; Convergence; Recurrent neural networks; Long short term memory; Informatics; Fractional-order difference; gated recurrent unit (GRU); lithium-ion battery (LIB); state of charge (SOC); OF-CHARGE ESTIMATION; STATE; MODEL;
D O I
10.1109/TII.2024.3485762
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The gated recurrent unit (GRU) networks are widely used in engineering applications due to the excellent performance. But, the flexibility of the proportion of update information to reset information is weak in GRU networks. To tackle this issue, this article proposes a fractional-order GRU (FOGRU) with a memory unit for the state of charge (SOC) estimation of lithium-ion batteries (LIBs). First, the Hausdorff difference is introduced into the GRU network to gain the fractional-order memory unit. Then, the range of the order is rigorously analyzed to ensure the convergence of the improved structure in the FOGRU network, and the adjustment rule of orders in the FOGRU network is to adaptively tune the FOGRU network. Finally, the experiment results show that the FOGRU network achieves a satisfactory effect in the SOC estimation of LIBs.
引用
收藏
页码:1576 / 1584
页数:9
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