Abstract
State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman filter (IUKF) that is able to suppress the non-positive definiteness of error covariance and Sage–Husa adaptive filter. The IAUKF can improve the SOC estimation stability and can improve the SOC estimation accuracy by estimating and correcting the system noise statistics adaptively. The IAUKF is verified under the federal urban driving schedule test, and the SOC estimation results are compared with IUKF and UKF. The experimental results show that the IAUKF has higher estimation accuracy and stability, which verifies the effectiveness of the proposed method.
Highlights
In order to realize the adaptive estimation of State of charge (SOC) by the improved adaptive unscented Kalman filter (IAUKF), firstly, the second-order resistor–capacitor (RC) equivalent circuit is selected to characterize the dynamic behaviors of the battery, and the model parameters are identified to obtain the state–space equation and output equation required by the IAUKF
Singular value decomposition (SVD) is used to replace the Cholesky decomposition in the traditional UKF, and an improved unscented Kalman filter (IUKF) that can suppress the non-positive definiteness of the error covariance is proposed
In order to improve the stability of the UKF for SOC estimation, the Cholesky factor decomposition in traditional UKF is replaced by singular value decomposition (SVD) in this paper, and the IUKF is proposed
Summary
With the increasing global energy crisis and environmental pollution, pollution-free and zero-emission electric vehicles have been developing rapidly [1]. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is low. A new SOC estimation algorithm for lithium-ion batteries based on an improved adaptive unscented Kalman filter is proposed in this paper. In order to realize the adaptive estimation of SOC by the improved adaptive unscented Kalman filter (IAUKF), firstly, the second-order resistor–capacitor (RC) equivalent circuit is selected to characterize the dynamic behaviors of the battery, and the model parameters are identified to obtain the state–space equation and output equation required by the IAUKF to estimate SOC. Singular value decomposition (SVD) is used to replace the Cholesky decomposition in the traditional UKF, and an improved unscented Kalman filter (IUKF) that can suppress the non-positive definiteness of the error covariance is proposed. The SOC estimation results are compared with IUKF and traditional UKF
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