State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter

Abstract

Accurate estimation of the state of charge plays a very important role in ensuring the safe and effective operation of battery lithium-ion batteries and is one of the most important state parameters. However, the estimation method of state of charge has various limitations, so it is of great significance to improve the accuracy and calculation speed of the method. In this article, we propose an improved recurrent neural network model to estimate lithium-ion battery state of charge. Simple recurrent units are used to replace the traditional recurrent neural network basic unit or long short-term memory unit, and the computation speed is improved by implementing parallel processing. Finally, the prediction results of the model are fed into an unscented Kalman filter module to remove the interference of noise on the prediction. This article studies the prediction accuracy and speed of Samsung INR 18650-20R and INR 18650-25R under various ambient temperatures, initial state of charge values, and electric vehicle drive cycles. The results show that the proposed method can obtain accurate state of charge estimation results in the INR 18650-20R data set. For different temperatures and initial SOC, the root mean square error is less than 0.015 and 0.016, and the prediction speed is about 30% higher than that of long short-term memory. In the INR 18650-25R data set, for three different driving cycles, the root mean square error is less than 0.034, and the average test speed is about 2.7s, which proves the effectiveness of this method in estimating accuracy and speed.