Abstract
State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions.
Highlights
In recent years, electric vehicles (EV) have become a trend in the automotive industry due to their advantages of no emissions, low energy consumption and low noise [1]
Considering the influence of noise covariance on the performance of the algorithm, this paper enhances the digital stability by combining weighted least squares (WLS) with correntropy, and it can meet the demand for effective estimation under highly nonlinear and non-Gaussian conditions in theory
Taking into account the interference of non-Gaussian noise, a correntropy EKF is utilized to estimate the State of charge (SOC) to improve the estimation accuracy; Considering the influence of noise covariance on the performance of the EKF algorithm, this paper developed a novel robust extended Kalman filter (C-WLS-EKF) by combining the weighted least squares and correntropy to enhance the digital stability of the C-EKF; The proposed C-WLS-EKF is employed for SOC estimation of lithium batteries under non-Gaussian noise cases
Summary
Electric vehicles (EV) have become a trend in the automotive industry due to their advantages of no emissions, low energy consumption and low noise [1]. As far as we know, the MCC-EKF has not been utilized to estimate SOC for lithium batteries in non-Gaussian noise cases. Considering the influence of noise covariance on the performance of the algorithm, this paper enhances the digital stability by combining weighted least squares (WLS) with correntropy, and it can meet the demand for effective estimation under highly nonlinear and non-Gaussian conditions in theory. Taking into account the interference of non-Gaussian noise, a correntropy EKF is utilized to estimate the SOC to improve the estimation accuracy; Considering the influence of noise covariance on the performance of the EKF algorithm, this paper developed a novel robust extended Kalman filter (C-WLS-EKF) by combining the weighted least squares and correntropy to enhance the digital stability of the C-EKF; The proposed C-WLS-EKF is employed for SOC estimation of lithium batteries under non-Gaussian noise cases.
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