English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Battery impedance spectroscopy models are given by fractional order (FO)differential equations. In the discrete-time domain, they give rise tostate-space models where the latent process is not Markovian. Parameterestimation for these models is therefore challenging, especially fornon-commensurate FO models. In this paper, we propose a Bayesian approach toidentify the parameters of generic FO systems. The computational challenge istackled with particle Markov chain Monte Carlo methods, with an implementationspecifically designed for the non-Markovian setting. The approach is thenapplied to estimate the parameters of a battery non-commensurate FO equivalentcircuit model. Extensive simulations are provided to study the practicalidentifiability of model parameters and their sensitivity to the choice ofprior distributions, the number of observations, the magnitude of the inputsignal and the measurement noise.

Bayesian inference in non-Markovian state-space models with applications to fractional order systems