State of health estimation for lithium-ion battery based on the coupling-loop nonlinear autoregressive with exogenous inputs neural network

•Coupling loop NARX neural network has better time series prediction accuracy.•Principal component feature extraction eliminates redundant variables effectively.•BR algorithm speeds up the convergence rate of neural network weights. In order to solve the problem of low accuracy of traditional artificial neural networks in approximating functional, a coupling-loop nonlinear autoregressive with exogenous inputs neural network estimation model is proposed and applied to the state of health forecasting of lithium-ion batteries. Firstly, eight health indicators related to the state of health of lithium-ion batteries are mined. In order to eliminate the harm of weakly correlated independent variables and redundant independent variables to the estimation results, principal component feature extraction is applied to the feature extraction of the eight health indicators. Bayesian regularization algorithm is used to learn the weights of the neural network, which solves the problems of slow convergence speed and easy to fall into local extremum of back propagation learning algorithm, and improves the generalization ability of the neural network. In order to verify the rationality of the proposed model and algorithm, the coupling-loop nonlinear autoregressive with exogenous inputs neural network model is used to estimate the state of health of lithium battery under the condition of complete charge discharge test, and the estimation results are compared with other neural network models. The simulation results show that the coupling-loop nonlinear autoregressive with exogenous inputs estimation model using feature extraction method and Bayesian network weight algorithm can approach the functional with higher accuracy, and the absolute error and relative error of lithium-ion battery's state of health estimation can be reduced by more than 50% on average, while the mean square error can be reduced by more than 80%.