Combined EKF–LSTM algorithm-based enhanced state-of-charge estimation for energy storage container cells

Abstract

The core equipment of lithium-ion battery energy storage stations is containers composed of thousands of batteries in series and parallel. Accurately estimating the state of charge (SOC) of batteries is of great significance for improving battery utilization and ensuring system operation safety. This article establishes a 2-RC battery model. First, the Extended Kalman Filter (EKF) algorithm is used to obtain preliminary SOC estimates. Then, the updated error values of the Kalman matrix, the state variables obtained from the EKF algorithm, and the battery data during system operation are used as the training and test dataset for the Long Short-Term Memory (LSTM) neural network algorithm. Finally, the algorithm was compared and analyzed with commonly used EKF estimation methods and LSTM algorithms. It was found that the root-mean-square error of the SOC of the EKF–LSTM algorithm under different operating conditions was less than 0.8%, and the average absolute error was less than 0.5%. The estimation accuracy is higher than either the EKF algorithm or LSTM algorithm alone.