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- W3024926075 abstract "In this paper, based on the Bayesian state tracking training updating proposed distribution method to improve particle degradation phenomenon, the superior characteristics of Markov Chain-Monte Carlo theory (MCMC) is fully used to enrich the diversity of sampling particles to solve the problem of resampling depletion, and build an updated and improved particle filtering (PF) algorithmic research model based on MCMC to predict the remaining useful life (RUL) of battery. Meanwhile, the method of combining reformed self-adaptive algorithm with updated and improved PF algorithmic research model is adopted to identify and optimize the noise multi-feature parameters of different experimental data, then obtain the identification table of process noise distribution. Based on the above analysis, an integrate system of systematic research methods for self-adaptive identification noise distribution, battery state tracking and RUL prediction is proposed in this paper. The analysis results show that the system has the advantages of good fitting degree of state tracking, high RUL prediction accuracy (error less than 5%) and strong stability, robustness and generalization adaptability." @default.
- W3024926075 created "2020-05-21" @default.
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- W3024926075 date "2019-12-01" @default.
- W3024926075 modified "2023-10-14" @default.
- W3024926075 title "Research on Battery State Tracking and Remaining Life Prediction Method Based on Improved Particle Filtering and Reformed Self-Adaptive Fusion Algorithms" @default.
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- W3024926075 doi "https://doi.org/10.1109/iscid.2019.10105" @default.
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