Estimation of State of Charge of Lithium-Ion Batteries Based on Wide and Deep Neural Network Model

Abstract

It is important to accurately estimate the SOC to ensure that the lithium-ion battery is within a safe working range, prevent over-charging and over-discharging, and ultimately improve battery life. However, SOC is an internal state of the battery and cannot be directly measured. This paper proposes a SOC estimation method based on the wide and deep neural network model, which combines the linear regression (LR) model and the backpropagation neural network (BPNN) model. This article uses the dataset provided by the Advanced Energy Storage and Applications (AESA) group to verify the performance of the model. The performance of the proposed model is compared with the common BPNN model in terms of root mean square error (RMSE), average absolute proportional error (MAPE), and SOC estimation error. The validation results prove that the effect of the proposed model in estimating SOC is better than that of the ordinary BPNN model. Compared with the BPNN model, the RMSE values of the SOC predicted value of the wide and deep model in the charging and discharging stages were reduced by 10.2% and 15.4%, respectively. Experimental results show that the maximum SOC estimation error of the model in predicting the SOC during charging and discharging is 0.42% and 0.86%, respectively.