Journal
APPLIED MATHEMATICAL MODELLING
Volume 70, Issue -, Pages 532-544Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2019.01.031
Keywords
Li-ion battery; State of charge estimation; Unscented Kalman filter; Model adaptive; Noise adaptive
Funding
- Aviation Science Foundation of China [2013ZD52055]
- Foundation of National Engineering and Research Center for Commercial Aircraft Manufacturing [SAMC14-35-15-051]
- Foundation of the Fundamental Research Funds for the Central Universities [NS2017019]
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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. (C) 2019 Elsevier Inc. All rights reserved.
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