华中科技大学学报（自然科学版）2024，Vol.52Issue(7)：76-82,7.DOI:10.13245/j.hust.240067
基于RSN-GRU融合网络的锂电池荷电状态估计
State of charge estimation of lithium battery based on RSN-GRU fusion network
摘要
Abstract
To improve the accuracy of state of charge(SOC)estimation of lithium batteries,a parallel fusion network of residual shrinking network(RSN)and gated recurrent unit(GRU)was proposed for SOC estimation of lithium batteries.RSN extracts local features of input sequence of lithium battery and removes noise through a small sub-network.Meanwhile,GRU extracts history information of input sequence,and finally RSN and GRU are fused in parallel to obtain the SOC estimate of lithium battery.The experimental results under various dynamic working conditions and different temperatures conditions show that the RSN-GRU parallel fusion network can significantly improve the SOC estimation accuracy of lithium batteries.The mean absolute error(MAE)and root mean square error(RMSE)of the estimation results at 25℃are 0.34%and 0.51%,respectively.Compared with GRU and RSN,the estimation accuracy is improved by 50%and 61.7%,respectively.In addition,the results of comparison between RSN-GRU and other commonly used networks show that the SOC estimation accuracy of this network is higher than others and it has obvious superiority.关键词
门控循环单元/残差收缩网络/并行融合网络/锂电池/荷电状态估计Key words
gated recurrent unit/residual shrinking network/parallel fusion network/lithium battery/state of charge estimation分类
信息技术与安全科学引用本文复制引用
全睿,刘品,张键,梁文龙..基于RSN-GRU融合网络的锂电池荷电状态估计[J].华中科技大学学报（自然科学版）,2024,52(7):76-82,7.基金项目
国家自然科学基金资助项目(51977061,51407063) （51977061,51407063）
太阳能高效利用及储能运行控制湖北省重点实验室开放基金资助项目(HBSEES202205). （HBSEES202205）
