  This paper presents a Bayesian parameter estimation approach and
identifiability analysis for a lithium-ion battery model, to determine the
uniqueness, evaluate the sensitivity and quantify the uncertainty of a subset
of the model parameters. The analysis was based on the single particle model
with electrolyte dynamics, rigorously derived from the Doyle-Fuller-Newman
model using asymptotic analysis including electrode-average terms. The Bayesian
approach allows complex target distributions to be estimated, which enables a
global analysis of the parameter space. The analysis focuses on the
identification problem (i) locally, under a set of discrete quasi-steady states
of charge, and in comparison (ii) globally with a continuous excursion of state
of charge. The performance of the methodology was evaluated using synthetic
data from multiple numerical simulations under diverse types of current
excitation. We show that various diffusivities as well as the transference
number may be estimated with small variances in the global case, but with much
larger uncertainty in the local estimation case. This also has significant
implications for estimation where parameters might vary as a function of state
of charge or other latent variables.
