Remaining useful life prediction of aircraft lithium-ion batteries based on F-distribution particle filter and kernel smoothing algorithm

作     者：Kai ZHANG Peng ZHAO Canfei SUN Youren WANG Zewang CHEN Kai ZHANG;Peng ZHAO;Canfei SUN;Youren WANG;Zewang CHEN

作者机构：College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjing 211106China Aviation Industry Corporation Leihua Electronic Technology Research InstituteWuxi 214000China Testing and Verification CenterAviation Key Laboratory of Science and Technology on Fault Diagnosis and Health ManagementShanghai 201601China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2020年第33卷第5期

页      面：1517-1531页

核心收录：

学科分类：082502[工学-航空宇航推进理论与工程] 08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：co-supported by Aeronautical Science Foundation of China (No. 20183352030) Fund Project of Equipment Pre-research Field of China (No. JZX7Y20190243016301) 

主　　题：F-distribution Kernel smoothing Lithium-ion batteries Markov model Particle filter Prediction Remaining useful life 

摘      要：As an emergency and auxiliary power source for aircraft,lithium(Li)-ion batteries are important components of aerospace power *** Remaining Useful Life(RUL)prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power *** Filter(PF)is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ***,there are problems that particle weights cannot be updated in the prediction stage and particles *** settle these issues,an innovative technique of F-distribution PF and Kernel Smoothing(FPFKS)algorithm is *** the prediction stage,the weights of the particles are dynamically updated by the F kernel instead of being fixed all the ***,a first-order independent Markov capacity degradation model is ***,the kernel smoothing algorithm is integrated into PF,so that the variance of the parameters of capacity degradation model keeps *** based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Liion batteries.