n On the surface it might look like the particle filter has uniquely determined the state. n Resampling induces loss of diversity. The variance of the particles decreases, the variance of the particle set as an estimator of the true belief increases. n Occurs as the result of the variance in random sampling.

Mar 6, 2025 · This paper proposes a two-stage framework that combines uncertainty reduction and predictive modelling to enhance the accuracy of battery Remaining Useful Life (RUL) prediction. In the first stage, a...

A particle filter is a recursive, Bayesian state estimator that uses discrete particles to approximate the posterior distribution of the estimated state. The particle filter algorithm computes the state estimate recursively and involves two steps:

Sequential Importance Resampling (SIR) algorithm adds the following resampling step to SIS algorithm: Resampling Interpret each weight w(i) k as the probability of obtaining the sample index i in the set {x(i) k | i = 1,...,N}. Draw N samples from that discrete distribution and replace the old sample set with this new one.

Aug 15, 2023 · This paper proposes a new framework to realize battery state of health (SOH) estimation and remaining useful life (RUL) prediction. The variable forgetting factor online sequential extreme learning machine (VFOS-ELM) is used to estimate battery SOH, and particle filter (PF) algorithm used to predict battery RUL.

Mar 15, 2022 · To tackle these aforementioned limitations, an enhanced PF (ePF) technology will be proposed in this paper for Li-ion battery health monitoring and RUL prediction. It aims to alleviate the PF limitations and improve the modeling …

Study the application of stochastic processes to residual life prediction in an imperfect monitoring context. The residual lifetime, also called remaining useful life (RUL) is defined as the remaining operating time of a system before its failure. Predicting this duration plays an important role in maintenance strategies.

Firstly, a variable forgetting factor recursive least square (VFFRLS) algorithm is proposed for battery model parameter identification. It adaptively adjusts the forgetting factor according to...

The algorithm consists of a particle filter (PF) that combines the use of the standard PF with mutation operators.

To address this problem, this article proposes a prognostic approach based on an adaptive particle filter (PF) to predict the RUL of the dynamic degradation systems using system degradation records.