Generating characteristic maps of battery cell parameters from sparse input data

Abstract

Describing characteristic maps based on sparse input data (data points) is a problem that occurs in a variety of different disciplines. For the conventional interpolation of input data, various standard interpolation methods are available in most applications. Depending on the data’s complexity to be modeled and the available input data, these methods often produce results that do not meet the required accuracy. This paper presents an interpolation method to generate characteristic maps in which data points are added iteratively evident. The data can be arranged arbitrarily in an n-dimensional space. A comparison with standard interpolation methods for map generation is presented. The presented method shows a better fit with selected reference maps compared to applied standard interpolation methods. As an application example, the method is shown in the context of a Hardware-in-the-Loop application. Electrical loads resulting from two different dynamic load scenarios from a plug-in hybrid electric vehicle simulation were applied simultaneously to the simulated and existing battery module to compare the simulated and measured thermal behavior that directly depends on the generated characteristic maps’ accuracy. The results show that the battery module’s simulated temperature deviates by a maximum of approximately 0.3 K from the measured temperature.