Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach

• Published in: Applied Mathematical Modelling Vol. 70; pp. 532 - 544
• Main Authors: Chen, Zewang, Yang, Liwen, Zhao, Xiaobing, Wang, Youren, He, Zhijia
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.06.2019; Elsevier BV
• Subjects: Adaptive algorithms; Adaptive filters; Algorithms; Kalman filters; Li-ion battery; Lithium-ion batteries; Mathematical models; Model adaptive; Noise adaptive; Parameter identification; Parameter sensitivity; Robustness; Sensitivity analysis; State of charge; State of charge estimation; Unscented Kalman filter; State of charge estimation; Model adaptive; Unscented Kalman filter; Li-ion battery; Noise adaptive
• ISSN: 0307-904X; 1088-8691; 0307-904X
• DOI: 10.1016/j.apm.2019.01.031

•State equations are derived from the battery equivalent circuit model.•Improved unscented Kalman filter approach composed of model adaptive and noise adaptive was introduced.•Experiment of sensitivity analysis was designed.•The experimental results revealed the effectiveness of the purposed approach. An improved unscented Kalman filter approach is proposed to enhance online state of charge estimation in terms of both accuracy and robustness. The goal is to address the drawback associated with the unscented Kalman filter in terms of its requirement for an accurate model and a priori noise statistics. Firstly, Li-ion battery modelling and offline parameter identification is performed. Secondly, a sensitivity analysis experiment is designed to verify which model parameter has the greatest influence on state of charge estimation accuracy, in order to provide an appropriate parameter for the model adaptive algorithm. Thirdly, an improved unscented Kalman filter approach, composed of a model adaptive algorithm and a noise adaptive algorithm, is introduced. Finally, the results are discussed, which reveal that the proposed approach’s estimation error is less than 1.79% with acceptable robustness and time complexity.