Abstract

Thermal pack management is dependent on the duty cycle and vehicle use. Thermal modelling of the pack for design, as well as active measures in operation to reduce thermal loads during operation can greatly improve the lifetime of the pack. Advances in state estimation, measurement techniques and control are coupled with the system design; and both are dependent on representative models. As the cells age, the thermal behaviour will change, and therefore a robust control technique and in-situ thermal model identification is often required. This paper focuses on methodologies towards thermal characterization of cell models and of a pack as a whole.

Abstract

This work presents an approach for automatically generating material laws at large strain. To this end, a concept of material modelling based on directly connected rheological elements is applied. This concept allows the implementation of any material law defined by a rheological model by recursive evaluation of connection relations. Therewith a tree encoding for the rheological model is applied. The search for suitable material laws fitting experimental data is realized by a genetic algorithm, which generates rheological models and evaluates their quality with respect to the given measurements. The central concepts and the genetic operators are presented in this contribution. Finally, the method is verified with synthetic data and utilized to real experimental data.