基于CNN-LSTM的锂离子SOC估计

SOC Estimation of Lithium-ion Batteries Based on CNN-LSTM

电池荷电状态(SOC)是锂离子电池管理技术中最重要的参数之一,高精度的SOC估计有利于储能电站的并网和控制。电池充放电数据不仅具有时序性,特征变量之间也存在一定空间关系,为提高估算方法的准确性和通用性,提出一种基于CNN-LSTM联合网络结构的锂离子电池SOC估计方法,先通过CNN特征提取获取了锂离子电池不同维度数据间的特征关系,然后经过LSTM网络结构提取其中的时间序列关系,联合网络充分获取了电池数据集的空间时间特性。实验结果表明,基于CNN-LSTM联合网络模型预测电池SOC平均误差控制在0.65%,较单独的CNN网络预测平均误差降低4.4%左右,较单独的LSTM网络预测的平均误差降低0.2%左右,具有较好的应用前景。

The state of charge(SOC)of batteries is one of the most important parameters in lithium-ion battery management technology,and high-precision SOC estimation is beneficial for the grid connection and control of energy storage stations.Battery charge and discharge data are not only time-series in nature,but also have certain spatial relationships between feature variables.To improve the accuracy and generality of the estimation method,a SOC estimation method was proposed for lithium-ion batteries based on a joint convolutional neural networks-long short term memory networks(CNN-LSTM)network structure.Firstly,the feature relationships between different dimensions of lithium-ion battery data were obtained through CNN feature extraction,and then the time series relationships were extracted through the LSTM network structure.The joint network fully captures the spatial and temporal characteristics of the battery dataset.The experimental results show that the average error of predicting battery SOC based on the CNN-LSTM joint network model is controlled at 0.65%,which is about 4.4%lower than the average error predicted by a single CNN network and about 0.2%lower than the average error predicted by a single LSTM network.It has good application prospects.