Study on Partial Stratified Resampling for Particle Filter Based Prognosis on Li-Ion Batteries
作者： Karkulali Pugalenthi , Nagarajan Raghavan
DOI: 10.1109/PHM-CHONGQING.2018.00207
关键词: Particle filter 、 Computer science 、 Effective method 、 Degeneracy (mathematics) 、 Prognostics 、 Battery (electricity) 、 Resampling 、 Focus (optics) 、 Mathematical optimization 、 Accuracy and precision
摘要: Accurate online prognosis of engineering systems plays a vital role in and health management (PHM) technologies to ensure safety, prevent damage economic loss. The particle filter (PF) algorithm has proved be an effective method for prognostics. However, the PF suffers from serious degeneracy impoverishment problems. Most studies literature focus on solving problem but at heavy computational cost. In this study, we aim explore time efficient Partial Stratified Resampling which can used state estimation problems compare it with conventional algorithms. accuracy precision algorithms are validated using lithium-ion battery data sets CALCE® research group.
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