State of Charge Estimation of Battery Energy Storage Systems Based on Adaptive Unscented Kalman Filter With a Noise Statistics Estimator

• Published in: IEEE access Vol. 5; pp. 13202 - 13212
• Main Authors: Peng, Simin, Chen, Chong, Shi, Hongbing, Yao, Zhilei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Adaptive systems; Adaptive unscented Kalman filter; Batteries; Battery charge measurement; battery energy storage systems; Covariance; Energy storage; Error analysis; Estimation; Extended Kalman filter; Integrated circuit modeling; Kalman filters; Noise; noise statistics estimator; State of charge; Statistics; Storage systems; Voltage measurement
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2725301

Since the noise statistics of large-scale battery energy storage systems (BESSs) are often unknown or inaccurate in actual applications, the estimation precision of state of charge (SOC) of BESSs using extended Kalman filter (EKF) or unscented Kalman filter (UKF) is usually inaccurate or even divergent. To resolve this problem, a method based on adaptive UKF (AUKF) with a noise statistics estimator is proposed to estimate accurately SOC of BESSs. The noise statistics estimator based on the modified Sage-Husa maximum posterior is aimed to estimate adaptively the mean and error covariance of measurement and system process noises online for the AUKF when the prior noise statistics are unknown or inaccurate. The accuracy and adaptation of the proposed method is validated by the comparison with the UKF and EKF under different real-time conditions. The comparison shows that the proposed method can achieve better SOC estimation accuracy when the noise statistics of BESSs are unknown or inaccurate.