  Parameter estimation is of foundational importance for various model-based
battery management tasks, including charging control, state-of-charge
estimation and aging assessment. However, it remains a challenging issue as the
existing methods generally depend on cumbersome and time-consuming procedures
to extract battery parameters from data. Departing from the literature, this
paper sets the unique aim of identifying all the parameters offline in a
one-shot procedure, including the resistance and capacitance parameters and the
parameters in the parameterized function mapping from the state-of-charge to
the open-circuit voltage. Considering the well-known Thevenin's battery model,
the study begins with the parameter identifiability analysis, showing that all
the parameters are locally identifiable. Then, it formulates the parameter
identification problem in a prediction-error-minimization framework. As the
non-convexity intrinsic to the problem may lead to physically meaningless
estimates, two methods are developed to overcome this issue. The first one is
to constrain the parameter search within a reasonable space by setting
parameter bounds, and the other adopts regularization of the cost function
using prior parameter guess. The proposed identifiability analysis and
identification methods are extensively validated through simulations and
experiments.
