State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method

Abstract

An accurate state of charge (SOC) estimation depends on an accurate battery model. The influence of nonlinear and unstable interference factors makes the accurate SOC estimation difficult. To obtain an accurate battery model, a method based on the NARX (nonlinear autoregressive network with exogenous inputs) recurrent neural network and moving window method is proposed. This paper improves the accuracy, modelling speed and robustness of SOC estimation from the following three aspects. First, to overcome the excessive reliance on the amount of data in the model training process, the NARX recurrent neural network is used to establish the battery model. NARX (nonlinear autoregressive with external input) recurrent neural network with the delay and feedback functions can keep the input and output of a previous moment and add it to the calculation of the next moment. Therefore, better estimation results are achieved using a small amount of data; second, the moving window method is used against the gradient explosion and the gradient vanishing that may occur in the NARX model training process. Third, by comparing it with other methods under different working conditions and different temperatures, the validity of the proposed model is verified. The results indicate that the proposed model has a higher accuracy and speed of the SOC estimation. The RMSE performance of the proposed model is reduced by approximately 65%, and the execution time is shortened by approximately 50%.

A NARX network optimized with an adaptive weighted square-root cubature Kalman filter for the dynamic state of charge estimation of lithium-ion batteries

“…As a recurrent dynamic network with feedback mechanisms enclosing several layers, the NARX network has been utilized for estimating the state parameters oflithium-ion batteries. For instance, Wang et al [43] proposed a NARX network for SOC estimation of lithium-ion batteries with a moving window method, which prevents the vanishing and explosion of the gradient at various ambient temperatures of 0, 25, and 45 °C under two working conditions. Herle et al [44] proposed a NARX network for SOC esti-mation by verifying it under four working conditions at RT conditions using different hyperparameters.…”

Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

“…The number of nodes in the hidden layer is related to the complexity of the network and the size of the error in the model output. After a reasonable training with a large amount of data, the number of nodes in the hidden layer is determined using parameters related to the network convergence speed [21]. The ability of the neural network method to deal with nonlinear problems makes it widely used in the SOC estimation of lithium-ion batteries for electric vehicles [22].…”

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

“…Patel et al investigated the impact of different battery working conditions and models on the accuracy of SOC estimation [20,21]. Finally, Wang et al used a sliding window model for data processing and applied a neural network for battery SOC estimation [22].…”