Space-Filling Subset Selection for an Electric Battery Model
This work provides a method for automotive engineers to more efficiently and accurately model electric battery performance using real-world, non-uniformly excited data.
This paper addresses the challenge of non-uniform excitation in real driving data for electric battery models by developing a subset selection method. This method improves the quality of nonlinear autoregressive exogenous (NARX) models and accelerates training compared to using all available data.
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.