广东电力2026，Vol.39Issue(3)：31-40,10.DOI:10.3969/j.issn.1007-290X.2026.03.004
基于卷积历史序列分解混合-长短期记忆网络的锂电池SOC估计
State of Charge Estimation for Lithium-ion Batteries Based on Convolutional Past Decomposable Mixing-LSTM Network
摘要
Abstract
Accurate estimation of state of charge(SOC)for lithium-ion batteries is crucial for the energy storage system and electric vehicle energy management.To address the limited SOC estimation accuracy of single neural networks under complex operating conditions,this paper proposes a hybrid estimation model based on a convolutional past decomposable mixing(CPDM)-long short-term memory(LSTM)network.First,the average pooling and a one-dimensional convolutional neural network are used to construct and extract multiscale temporal features from battery data.Second,a CPDM module is applied to perform cross-scale decomposition and mixing to enhance information complementarity.Finally,the enhanced multiscale sequences are fed in parallel into the LSTM network for prediction and the SOC estimation results are obtained by summing the per-scale predictions with equal weights.Experimental results show that the CPDM-LSTM model delivers good SOC estimation performance on public datasets.Under different temperatures and operating conditions,the average root-mean-square error is 0.048 5 and the mean absolute error is 0.037 1,demonstrating strong robustness and generalization of the model.关键词
锂离子电池/荷电状态/卷积神经网络/时间序列分解/Timemixer/长短期记忆Key words
lithium-ion battery/state of charge/convolutional neural network/time series decomposition/Timemixer/long short-term memory分类
信息技术与安全科学引用本文复制引用
彭文轩,杨超,钟晓青,张斌..基于卷积历史序列分解混合-长短期记忆网络的锂电池SOC估计[J].广东电力,2026,39(3):31-40,10.基金项目
国家自然科学基金项目(62320106008、62303123) （62320106008、62303123）
