摘要
电池健康状态(SOH)是锂离子电池的一项重要指标。为提高预测精度,提出了一种基于深度卷积神经网络(CNN)和长短期记忆网络(LSTM)的并行CNN-LSTM网络模型,用于预测矿用锂电池的健康状况。该方法利用CNN获取数据局部特征,LSTM获取时间序列信息。然后将CNN层和LSTM层获取的信息合并为一个张量,输入额外的LSTM层,进一步获取信息,完成电池健康状态预测。通过对电池的放电容量、放电时间、内阻等特征进行选择和分析,验证了该模型能够有效地预测电池的健康状况。仿真结果表明,该模型在数据集上的预测误差均小于3%,均方根误差(RMSE)和平均绝对误差(MAE)值的平均值在0.484%和0.278%以内。
State of battery health(SOH)is an important indicator of lithium-ion batteries.To improve prediction accuracy,a parallel CNN-LSTM network model based on deep convolutional neural network(CNN)and long short-term memory network(LSTM)was proposed for predicting the health state of mining lithium batteries.This method utilizes CNN to obtain local features of the data,while LSTM obtains time series information.Then,the information obtained from the CNN layer and LSTM layer was merged into a tensor,and an additional LSTM layer was input to further obtain information and complete the prediction of the state of battery health.By selecting and analyzing the discharge capacity,discharge time,internal resistance and other characteristics of the battery,it was verified that the model can effectively predict the health status of the battery.The simulation results showed that the prediction error of the model on the dataset was less than 3%,and the average values of root mean square error(RMSE)and mean absolute error(MAE)were within 0.484%and 0.278%.
作者
常映辉
王大钟
冀鹏飞
周锋涛
CHANG Yinghui;WANG Dazhong;JI Pengfei;ZHOU Fengtao(CCTEG Chinese Institute of Coal Science,Beijing 100013,China;CCTEG Taiyuan Research Institute,Taiyuan 030006,China;Shanxi Tiandi Coal Mining Machinery Co.,Ltd.,Taiyuan 030006,China)
出处
《煤矿机电》
2023年第4期6-11,共6页
Colliery Mechanical & Electrical Technology
基金
山西省基础研究计划(202103021223461)
天地科技重点项目(KY2023004)。
关键词
并行CNN-LSTM
电池健康状态
卷积神经网络
长短期记忆网络
电池内阻
parallel CNN-LSTM
state of battery health
convolutional neural network
long short-term memory networks
battery internal resistance
