期刊
CHINESE JOURNAL OF AERONAUTICS
卷 33, 期 5, 页码 1517-1531出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.cja.2020.01.007
关键词
F-distribution; Kernel smoothing; Lithium-ion batteries; Markov model; Particle filter; Prediction; Remaining useful life
资金
- Aeronautical Science Foundation of China [20183352030]
- Fund Project of Equipment Pre-research Field of China [JZX7Y20190243016301]
As an emergency and auxiliary power source for aircraft, lithium (Li)-ion batteries are important components of aerospace power systems. The Remaining Useful Life (RUL) prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power systems. Particle Filter (PF) is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ability. However, there are problems that particle weights cannot be updated in the prediction stage and particles degradation. To settle these issues, an innovative technique of F-distribution PF and Kernel Smoothing (FPFKS) algorithm is proposed. In the prediction stage, the weights of the particles are dynamically updated by the F kernel instead of being fixed all the time. Meanwhile, a first-order independent Markov capacity degradation model is established. Moreover, the kernel smoothing algorithm is integrated into PF, so that the variance of the parameters of capacity degradation model keeps invariant. Experiments based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Li-ion batteries. (C) 2020 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.
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