基于NARX神经网络的车用锂离子电池SOH时间序列预测  

SOH Time-Series Prediction of Vehicle Lithium-Ion Battery Based on NARX Network

作　　者：徐东辉 石本改[2] 徐丽琴[1] 叶雪强[1] 王丽娜 XU Donghui;SHI Ben'gai;XU Liqin;YE Xueqiang;WANG Lina(Department of Mathematics and Computer Science,Nanchang Normal Universiy,Nanchang 330032,China;School of Automobile,Guangdong Vocational College of Mechanical and Electrical Technology,Guangzhou 510550,China;School of Automotive and Mechanical Engineering,Changsha University of Science and Technology,Changsha 410076,China)

机构地区：[1]南昌师范学院数学与计算机科学系,江西南昌330032 [2]广东机电职业技术学院汽车学院,广东广州510550 [3]长沙理工大学汽车与机械工程学院,湖南长沙410076

出　　处：《车用发动机》2022年第6期71-75,共5页Vehicle Engine

基　　金：国家自然科学基金项目(51176014);江西省重点研发计划项目(20192BBHL80002);江西省教育厅科学技术研究项目(GJJ202610,GJJ212623)。

摘　　要：锂离子电池模型参数具有慢时变特性,因而准确预测锂离子电池健康状态(state of health,SOH)存在较大的难题。利用非线性自回归(Nonlinear AutoRegressive with eXogenous input,NARX)神经网络建立了SOH时间序列预测模型,通过重构技术将预测模型的一维输入时间序列重构成多维状态空间,并且采用重构后的时间序列数据对NARX神经网络对进行训练,然后利用训练后的NARX神经网络进行预测得到最终的SOH时间序列预测值;试验结果显示,预测模型比RBF神经网络的均方误差提高了近6个百分点,收敛速度提高了近30 s,表明了基于NARX的SOH时间序列预测模型的预测精度及响应速度都较好。The state of health(SOH)of lithium ion battery was difficult to predict accurately due to the slow and time-varying characteristic of model parameters.The SOH time-series prediction model was hence built by using NARX(nonlinear autoregressive with exogenous input)neural network.The one-dimensional input time series of prediction model was reconstructed into the multidimensional state space by the reconstruction technology,the NARX neural network was trained with the reconstructed time-series data,and then the final SOH time-series prediction value was obtained by using the trained NARX neural network.The experimental results show that the mean square error and the convergence speed of the proposed model is nearly 6 percentage points and 30 seconds superior to those of RBF neural network,which indicates that the prediction accuracy and response speed of the SOH time-series prediction model based on NARX are both better.

关 键 词：锂离子电池 健康状态 神经网络 预测 

分 类 号：TM912.9[电气工程—电力电子与电力传动]