Improving lightweight state-of-charge estimation of lithium-ion battery using residual network and gated recurrent neural network

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成果类型：

期刊论文

作者：

Yinglong Zhao;Yong Li*;Yijia Cao;Yixiao Wang

通讯作者：

Yong Li

作者机构：

College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China

[Yijia Cao] School of Electrical & Information Engineering, Changsha University of Science and Technology, Changsha, 410114, China

[Yixiao Wang] School of Automation, Central South University, Changsha, 410082, China

[Yinglong Zhao; Yong Li] College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China<&wdkj&>Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China

通讯机构：

[Yong Li] C

College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China<&wdkj&>Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China

语种：

英文

期刊：

Journal of Energy Storage

ISSN：

2352-152X

年：

2025

卷：

116

页码：

115934

DOI：

10.1016/j.est.2025.115934

机构署名：

本校为其他机构

院系归属：

电气与信息工程学院

摘要：

Lithium-ion batteries are now widely used as energy storage units in electric vehicles. Achieving high accuracy in state of charge (SOC) estimation in the battery management system (BMS) is critical for safe operation of electric vehicles. However, accurate SOC estimation remains a challenging task due to the complex dynamics of batteries and the wide range of ambient temperature. Here we propose a new method called ResNet-GRNN for accurate SOC estimation. Our approach combines a Residual network (ResNet) and a gated recurrent neural network (GRNN). Compared to traditional GRNNs, the proposed ...

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Improving lightweight state-of-charge estimation of lithium-ion battery using residual network and gated recurrent neural network

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