An Improved LSTNet Approach for State-of-Health Estimation of Automotive Lithium-Ion Battery

Abstract

Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is one of the pivotal technologies to ensure the safe and dependable operation of electric vehicles (EVs). To tackle the challenges related to the intricate preprocessing procedures and extensive data prerequisites of conventional SOH estimation approaches, this paper proposes an improved LSTNet network model. Firstly, the discharged battery sequence data are divided into long-term and short-term sequences. A spatially convolutional long short-term memory network (ConvLSTM) is then introduced to extract multidimensional capacity features. Next, an autoregressive (AR) component is employed to enhance the model’s robustness while incorporating a shortcut connection structure to enhance its convergence speed. Finally, the results of the linear and nonlinear components are fused to make predictive judgments. Experimental comparisons on two datasets are conducted in this study to demonstrate that the method fits the electric capacity recession curve well, even without the preprocessing step. For the data of four NASA batteries, the maximum root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the prediction results were maintained at 0.65%, 0.58%, and 0.435% when the proportion of the training set was 40%, which effectively validates the model’s feasibility and accuracy.