Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (5): 1514-1521.DOI: 10.11772/j.issn.1001-9081.2020071097

Special Issue: 前沿与综合应用

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

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

NI Shuiping, LI Huifang   

  1. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo Henan 454003, China
  • Received:2020-07-27 Revised:2020-09-26 Online:2021-05-10 Published:2021-05-19
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61872126).


倪水平, 李慧芳   

  1. 河南理工大学 计算机科学与技术学院, 河南 焦作 454003
  • 通讯作者: 李慧芳
  • 作者简介:倪水平(1977-),男,湖北黄冈人,副教授,博士,主要研究方向:人工智能;李慧芳(1994-),女,河南新乡人,硕士研究生,主要研究方向:智能信息处理。
  • 基金资助:

Abstract: 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.

Key words: one-Dimensional Convolutional Neural Network (1D CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), State-Of-Charge (SOC) prediction, battery

摘要: 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,通过一层一维卷积层从样本数据中提取出高级数据特征,并充分地利用输入数据的特征信息;其次,使用一层LSTM层保存历史输入信息,从而有效地预防重要信息的丢失;最后,通过一层全连接层输出电池SOC预测结果。使用电池的多次循环充放电实验数据训练提出的模型,分析对比不同超参数设置下1D CNN-LSTM模型的预测效果,并通过训练模型来调节模型的权重系数和偏置参数,从而确定最优的模型设置。实验结果表明,1D CNN-LSTM模型具有准确且稳定的电池SOC预测效果。该模型的平均绝对误差(MAE)、均方误差(MSE)和最大预测误差分别为0.402 7%、0.002 9%和0.99%。

关键词: 一维卷积神经网络, 循环神经网络, 长短期记忆, 荷电状态预测, 电池

CLC Number: