Journal of Power Sources, Vol. 392, pp. 48-59, July 2018, DOI:

An adaptive state of charge estimation approach for lithium-ion series-connected battery system

Simin Peng1, Xuelai Zhu1, Yinjiao Xing2, Michael G. Pecht2, Hongbing Shi3, Xu Cai4
1School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
2CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
3State Grid Yancheng Power Supply Company, Yancheng 224005, China
4Wind Power Research Center, Shanghai Jiao Tong University, Shanghai, China


Due to the incorrect or unknown noise statistics of a battery system and its cell-to-cell variations, state of charge (SOC) estimation of a lithium-ion series-connected battery system is usually inaccurate or even divergent using model-based methods, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). To resolve this problem, an adaptive unscented Kalman filter (AUKF) based on a noise statistics estimator and a model parameter regulator is developed to accurately estimate the SOC of a series-connected battery system. An equivalent circuit model is first built based on the model parameter regulator that illustrates the influence of cell-to-cell variation on the battery system. A noise statistics estimator is then used to attain adaptively the estimated noise statistics for the AUKF when its prior noise statistics are not accurate or exactly Gaussian. The accuracy and effectiveness of the SOC estimation method is validated by comparing the developed AUKF and UKF when model and measurement statistics noises are inaccurate, respectively. Compared with the UKF and EKF, the developed method shows the highest SOC estimation accuracy.

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