Abstract An improved unscented Kalman filter (IUKF) approach was proposed to enhance the accuracy and robustness of state of charge (SOC) online estimation, aimed at improving the drawbacks of unscented Kalman filter (UKF) which needs an accurate model and a priori noise statistics. Firstly, Li-ion battery modeling and offline parameters identification were realized. Secondly, sensitivity analysis experiment of the cell’s electrical model was designed to verify which model parameter has the most important influence on the SOC estimation accuracy, and provide the appropriate parameter for the model adaptive algorithm. Thirdly, IUKF approach composed of model adaptive algorithm and noise adaptive algorithm was introduced. Finally, this method was verified through physical experiment. The experimental results revealed that the proposed approach’s estimation error is less than 1.79% with acceptable robustness.
CHEN Ze-wang,YANG Li-wen,ZHAO Xiao-bing, et al. Online Estimation of SOC for Li-ion Battery Based on An Improved Unscented Kalman Filters Approach[J]. Acta Metrologica Sinica, 2019, 40(1): 40-48.
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2019, Vol. 40 
