由于电池荷电状态(state of charge,SOC)无法直接测量,且传统的SOC估算方法精度低.为了提升锂离子电池SOC估算精度,对比了不同深度学习网络模型应用于SOC估算的效果,并提出了一种基于DRSN-CW-LSTM网络的锂离子电池SOC估算方法.该方法基于长短期记忆网络(long-short-term memory,LSTM)和逐通道不同阈值的深度残差收缩网络(deep residual shrinkage networks with channel-wise thresholds,DRSN-CW),利用锂离子电池电压、电流、温度、容量等数据信息在深度残差收缩网路中进行特征提取,通过LSTM进一步拟合时间序列数据趋势,实现锂离子电池在使用周期内SOC的预测.在DRSN-CW网络的残差收缩模块中可以实现自适应噪声数据处理功能,消除锂离子电池数据流质量对SOC预测的负面影响.利用锂电池公共数据集训练所提出的网络,对比了3种神经网络模型在该两组数据集上的预测效果.实验结果表明,所提出的深度学习模型在两组公开数据集上的MAE和RMSE均值都控制在5%以内,相比其他3种深度学习模型有更好的抗噪性能和预测性能,且估算精度高.
State of Charge Prediction of Lithium-Ion Batteries Based on DRSN-CW-LSTM Network
Since the battery state of charge(SOC)cannot be measured directly,and the traditional SOC estimation methods have low accuracy.To improve the SOC estimation accuracy of lithium-ion batteries,this paper compares the effects of different deep-learning network models applied to SOC estimation and proposes a SOC estimation method for lithium-ion batteries based on the DRSN-CW-LSTM network.The method is based on long-short-term memory(LSTM)and deep residual shrinkage networks with channel-wise thresholds(DRSN-CW),using the data information of lithium-ion battery voltage,current,temperature,and capacity extracted in the deep residual shrinkage networks,and the time series data trends are further fitted by LSTM to achieve the prediction of SOC of lithium-ion battery during its service life.In the residual shrinkage module of the DRSN-CW network,an adaptive noise data process-ing function can be implemented to eliminate the negative impact of lithium-ion battery data stream quality on SOC prediction.In this paper,the proposed network is trained using the lithium-ion battery public dataset and the prediction effects of three neural network models on the two datasets are compared.The experimental results show that the MAE and RMSE of the deep learning model proposed in this paper are controlled within 5%of the average value on both public datasets,and have better noise immunity and prediction performance with high estimation accuracy compared with the other three deep learning models.
lithium-ion batterystate of charge predictionnoise processingdeep learninglong and short-term memory networkdeep residual systolic neural network
万方数据
维普
