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.
XU Donghui
,
SHI Bengai
,
XU Liqin
,
YE Xueqiang
,
WANG Lina
. SOH Time-Series Prediction of Vehicle Lithium-Ion Battery Based on NARX Network[J]. Vehicle Engine, 2022
, 0(6)
: 71
-75
.
DOI: 10.3969/j.issn.1001-2222.2022.06.012
