A technique for dynamic battery model identification in automotive applications using linear parameter varying structures

In this paper, a rapid calibration procedure for identifying the parameters of a dynamic model of batteries for use in automotive applications is described. The dynamic model is a phenomenological model based on an equivalent circuit model with varying parameters that are linear spline functions of the state of charge (SoC). The model identification process is done in a layered fashion: a two step optimization process using a genetic algorithm (GA) is used to optimize the parameters of the model over an experimental data set that encompasses the operating conditions of interest for the batteries. The level of accuracy obtained with this procedure is comparable to other black/gray box techniques, while requiring very little calibration effort. The process has been applied to both lithium ion and NiMH chemistries with good results. An extension of this technique to identify a model with both SoC and temperature dependence is discussed.