  Dynamic models of the battery performance are an essential tool throughout
the development process of automotive drive trains. The present study
introduces a method making a large data set suitable for modeling the
electrical impedance. When obtaining data-driven models, a usual assumption is
that more observations produce better models. However, real driving data on the
battery's behavior represent a strongly non-uniform excitation of the system,
which negatively affects the modeling. For that reason, a subset selection of
the available data was developed. It aims at building accurate nonlinear
autoregressive exogenous (NARX) models more efficiently. The algorithm selects
those dynamic data points that fill the input space of the nonlinear model more
homogeneously. It is shown, that this reduction of the training data leads to a
higher model quality in comparison to a random subset and a faster training
compared to modeling using all data points.
