Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler

2007 ◽  
Vol 8 (8) ◽  
pp. 1277-1282 ◽  
Author(s):  
Shi-chuan Du ◽  
Zhi-guo Shi ◽  
Wei Zang ◽  
Kang-sheng Chen
Keyword(s):  
Particle Filter ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Model Particle

Interacting multiple model particle filter for prognostics of lithium-ion batteries

2017 ◽  
Vol 70 ◽  
pp. 59-69 ◽  
Author(s):  
Xiaohong Su ◽  
Shuai Wang ◽  
Michael Pecht ◽  
Lingling Zhao ◽  
Zhe Ye
Keyword(s):  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Model Particle

Experimental verification of lithium-ion battery prognostics based on an interacting multiple model particle filter

2020 ◽  
pp. 014233122096157
Author(s):  
Shuai Wang ◽  
Wei Han ◽  
Lifei Chen ◽  
Xiaochen Zhang ◽  
Michael Pecht
Keyword(s):  
Particle Filter ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Li Ion Batteries ◽  
Model Particle ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Li Ion

A new data-driven prognostic method based on an interacting multiple model particle filter (IMMPF) is proposed for use in the determination of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries and the probability distribution function (PDF) of the uncertainty associated with the RUL. An IMMPF is applied to different state equations. The battery capacity degradation model is very important in the prediction of the RUL of Li-ion batteries. The IMMPF method is applied to the estimation of the RUL of Li-ion batteries using the three improved models. Three case studies are provided to validate the proposed method. The experimental results show that the one-dimensional state equation particle filter (PF) is more suitable for estimating the trend of battery capacity in the long term. The proposed method involving interacting multiple models demonstrated a stable and high prediction accuracy, as well as the capability to narrow the uncertainty in the PDF of the RUL prediction for Li-ion batteries.

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