基于一维卷积神经网络与长短期记忆网络结合的电池荷电状态预测方法

针对电池荷电状态(SOC)预测的精确度与稳定性问题以及深层神经网络的梯度消失问题,提出一种基于一维卷积神经网络(1D CNN)与长短期记忆(LSTM)循环神经网络(RNN)结合的电池SOC预测方法——1D CNN-LSTM模型.1D CNN-LSTM模型将电池的电流、电压和电阻映射到目标值SOC.首先,通过一层一维卷积...

Battery state-of-charge prediction method based on one-dimensional convolutional neural network combined with long short-term memory network

Focused on the issues of accuracy and stability of battery State-Of-Charge (SOC) prediction and gradient disappearance of deep neural network, a battery SOC prediction method based on the combination of one-Dimensional Convolutional Neural Network (1D CNN) and Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) named 1D CNN-LSTM (1D CNN combined with LSTM) model was proposed. The current, voltage and resistance of the battery were mapped to the target value SOC by 1D CNN-LSTM model. Firstly, a one-dimensional convolutional layer was used to extract the high-level data features from the sample data and make full use of the feature information of the input data. Secondly, a LSTM layer was used to save the historical input information, so as to effectively prevent the loss of important information. Finally, the prediction results of the battery SOC were outputted through a fully connected layer. The proposed model was trained with the experimental data of multiple cycles of charge-discharge of the battery, the prediction effects of the 1D CNN-LSTM model under different hyperparameter settings were analyzed and compared, and the weight coefficients and bias parameters of the model were adjusted through training the model, so that the optimal model setting was determined. Experimental results show that the 1D CNN-LSTM model has accurate and stable prediction effect of battery SOC. The Mean Absolute Error (MAE), Mean Square Error (MSE) and maximum prediction error of this model are 0.402 7%, 0.002 9% and 0.99% respectively.