湖北民族大学学报（自然科学版）2025，Vol.43Issue(2)：266-271,6.DOI:10.13501/j.cnki.42-1908/n.2025.03.022
基于SA-CDC-GRU-AE模型的锂离子电池健康状态估计方法
State of Health Estimation Method of Lithium-ion Batteries Based on the SA-CDC-GRU-AE Model
摘要
Abstract
In order to address the issue of insufficient prediction accuracy and poor generalization ability of traditional models for the state of health(SOH)prediction of lithium-ion batteries,a SOH estimation method of lithium-ion batteries based on self attention-causal dilated convolution-gated recurrent unit-autoencoder(SA-CDC-GRU-AE)model was proposed.In the convolution module,CDC module was introduced and combined with the SA mechanism to ensure causality in the prediction and suppress the interference of battery capacity regeneration on the prediction results.Additionally,the AE module was incorporated to optimize the GRU model,enabling it to both extract hidden features and capture long-term dependencies.Validation was performed on two public datasets.The results showed that SA-CDC-GRU-AE model achieved the average values of the root mean square error(RMSE)of 1.009%and 0.488%,and the average values of the mean absolute error(MAE)of 0.780%and 0.432%on the two datasets,respectively.SA-CDC-GRU-AE model could accurately estimate the SOH of lithium-ion batteries and had significant engineering application value for battery management systems.关键词
锂离子电池/健康状态估计/容量再生/因果卷积/膨胀卷积/自动编码器Key words
lithium-ion battery/SOH estimation/capacity regeneration/causal convolution/dilated convolution/autoencoder分类
信息技术与安全科学引用本文复制引用
胡钰航,廖宇,崔琨,李景聪..基于SA-CDC-GRU-AE模型的锂离子电池健康状态估计方法[J].湖北民族大学学报（自然科学版）,2025,43(2):266-271,6.基金项目
国家自然科学基金项目(62263010). （62263010）
