Enhanced square root CKF with mixture correntropy loss for robust state of charge estimation of lithium-ion battery

• Published in: Journal of energy storage Vol. 73; p. 108920
• Main Authors: Guo, Peng, Ma, Wentao, Yi, Dele, Liu, Xinghua, Wang, Xiaofei, Dang, Lujuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Mixture correntropy loss; non-Gaussian noise; Square-root cubature Kalman filter; State of charge estimation; Mixture correntropy loss; Square-root cubature Kalman filter; State of charge estimation; non-Gaussian noise
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2023.108920

The measurement data of lithium-ion battery collected in complex operating conditions may be contaminated by non-Gaussian noises (or outliers), a novel robust state of charge (SOC) estimation approach that enhances the robustness of square-root cubature Kalman filter (SRCKF) by incorporating a mixture correntropy loss (MCL) is proposed to overcome the issue of non-Gaussian measurement noise interference. Although the SRCKF can perform non-destructive state estimation for nonlinear system, it is sensitive to the non-Gaussian noises (or outliers) due to the use of mean square error (MSE) loss. To overcome this limitation, the MCL is used as a cost to replace the MSE in CKF framework, which is constructed by mixture of two Gaussian kernel functions with different kernel widths and has outstanding robustness. Moreover, we establish an effective nonlinear regression model that incorporates noise and state error information into the MCL function, and then a fixed-point iteration method is utilized to update the posterior state estimation. Additionally, to further suppress the influence of abnormal noise on the posterior state estimation, we introduce the exponential function of the innovation vector to adjust the measurement covariance matrix in real-time. Accordingly, an enhanced MCL-SRCKF is developed for SOC estimation considering the measurement noises with non-Gaussian distribution. Results from various simulation environments demonstrate that the proposed method achieves high estimation accuracy in different cases. •MCL is used to replace the MSE cost of the SRCKF to enhance its robustness;•An enhanced MCL-SRCKF method is further developed by using an exponential function of the innovation vector to adjust the measurement covariance matrix;•A novel robust SOC estimation method via the enhanced MCL-SRCKF is developed for solving the non-Gaussian measurement noise issues;