Research on online parameter identification and SOC estimation methods of lithium-ion battery model based on a robustness analysis

Abstract

Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium-ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF-AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞-CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium-ion battery SOC. FF-AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD-CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF-AGLS and H∞-SVD-CKF was able to accurately track model parameters and SOC with a strong degree of robustness.