A Combined State of Charge Estimation Method for Lithium-Ion Batteries Using Cubature Kalman Filter and Least Square with Gradient Correction

Reliable state of charge (SOC) is the core of the energy storage system for electric vehicles. An efficient method of parameter identification and SOC estimation for lithium-ion batteries (LIBs) using cubature Kalman filter (CKF) and least square with gradient correction is proposed. Firstly, the recursive gradient correction (RGC) strategy is introduced based on the recursive least square with forgetting factor (FFRLS) method, and a novel way is proposed for a simplified equivalent circuit model (ECM) based LIBs parameter identification using the combination of RGC and FFRLS method. Then, on the basis of CKF, the singular value decomposition (SVD) is used instead of the Cholesky decomposition to solve the non-positive definiteness matrix for cubature transformation in the priori estimated covariance, thereby improving the effect of SOC estimation. Finally, the dynamic stress test (DST) and the federal urban driving schedule (FUDS) are used to verify the effect of the proposed method. The test results reflect that the combination of RGCFFRLS-SVDCKF method and simplified ECM have advantages in terms of LIBs SOC estimation convergence ability and tracking accuracy, and the SOC estimation error can converge to 1% and 1.5% error boundary when the initial SOC are 0.5 and 0.8, respectively. In addition, the robustness of the proposed method is verified. Compared with the FFRLS-EKF method, the proposed RGCFFRLS-SVDCKF method has better precision and robustness to noise disturbance.

Automated summary

A method for parameter identification and SOC estimation for lithium-ion batteries using RGC and CKF is proposed. Experimental results show that the method has advantages in terms of convergence ability and tracking accuracy, and is more precise and robust to noise disturbance compared to traditional methods.