Search

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. [ABSTRACT FROM AUTHOR]

As an effective computing technique, Kalman filter (KF) currently plays an important role in state of charge (SOC) estimation in battery management systems (BMS). However, the traditional KF with mean square error (MSE) loss faces some difficulties in handling the presence of non-Gaussian noise in the system. To ensure higher estimation accuracy under this condition, a robust SOC approach using correntropy unscented KF (CUKF) filter is proposed in this paper. The new approach was developed by replacing the MSE in traditional UKF with correntropy loss. As a robust estimation method, CUKF enables the estimate process to be achieved with stable and lower estimation error performance. To further improve the performance of CUKF, an adaptive update strategy of the process and measurement error covariance matrices was introduced into CUKF to design an adaptive CUKF (ACUKF). Experiment results showed that the proposed ACUKF-based SOC estimation method could achieve accurate estimate compared to CUKF, UKF, and adaptive UKF on real measurement data in the presence of non-Gaussian system noises. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) plays a crucial role in battery management system, which directly reflects the usage of the battery. Recently the extreme learning machine (ELM) model as a data-driven method has been utilized to estimate SOC due to its simple single hidden layer structure and fast learning performance. The battery management system, however, may usually work in complex working conditions, which means that the non-Gaussian complex noise (or outliers) interference problem may exist in some measurement data for model training. So the performance of the classical ELM with mean square error (MSE) criterion may be degraded under this case. This work considers Non-Gaussian noise interference issue, the MSE in ELM is substituted by mixture generalized maximum correntropy criterion (MGMCC), and a novel robust ELM model is developed to improve the SOC estimation capability which mainly relies on the stable and robust nonlinear similarity characteristics of the MGMCC. A data set from a Panasonic 18,650 battery cell is used to verify the robustness of the proposed model, the experiment results demonstrate that it can achieve better estimation performance in terms of different evaluation metrics compared with the traditional methods. • A mixture GMCC is defined which can be as a learning criterion for NN model. • A robust ELM with MGMCC is developed to improve robust performance of the traditional ELM. • The MMGCCELM based SOC estimation approach is proposed to improve estimation accuracy under non-Gaussian noise interferences. [ABSTRACT FROM AUTHOR]