A BP neural network-Ant Lion Optimizer and UKF method for SOC estimation of lithium-ion batteries

Abstract

Accurate state of charge (SOC) estimation is of great significance to promote the development of new energy vehicles. And a battery model’s accuracy is of great significance for the battery SOC estimation accuracy. To this end, a method based on back propagation neural network-Ant Lion Optimizer (BPNN-ALO) and unscented Kalman filter (UKF) is proposed. First, a 2-RC battery model is established and BPNN is used to fit the OCV-SOC corresponding relationship. Second, the BPNN and ALO are combined to complete the battery model’s parameter identification, and UKF is used to complete SOC estimation. Finally, verify the BPNN-ALO-UKF under two working conditions, and compare it with the other two methods. The test results show that the proposed method has higher SOC estimation accuracy, the minimum values of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are only 0.51% and 0.40%, respectively. And under different working conditions, it also has a better generalization and robustness.