Florian Netter

Real-Data Validation of Simulation Models in a Function-Based Modular Framework

This paper presents a method for real-data validation of simulation models in a function-based modular framework. The system to be simulated is separated into singular subsystems called functions. The function-based modular framework guarantees that the interfaces of the functions are consistent, independent of any simulation tool, with which they are simulated, and simulation purpose. To validate the simulation models encapsulated in the functions, real data are integrated into the system simulation and the simulation results are compared with measured data. Because it is not possible to record every real data signal of the subsystems interfaces, for technical reasons, an algorithm is introduced that combines functions into so-called “function groups” in order to be able to validate them with real data. To confirm the significance and the functionality of the algorithm for simulation model validation, a simulation model of an electric car built by means of the function-based modular framework was validated with real data recorded on an electric vehicle fleet test. As a result, the presented method allowed the quality quantification of each simulation model encapsulated in the functions, enabling the prediction accuracy of the electric vehicle entire-system simulation to be classified.

Complexity adaptation of simulation models in a function-based modular framework

This paper presents a method for complexity adaptation of simulation models in a function-based modular framework. By this means, the problem of inappropriate simulation model complexity leading to inaccurate predictions and poor extrapolation within entire-system simulations is avoided. The function-based modular framework describes a process that separates entire-system simulations into defined individual subsystems called functions. Thus the basis for storing different complex simulation models within the separated functions is set, ensuring consistent interfaces, independent of any simulation tool, with which they are simulated. To adapt the complexity of simulation models encapsulated in the functions to simulation purposes, a method for complexity quantification is introduced, using concepts from information theory and statistical methods. To prove the functionality and reliability of the method, example different complex battery simulation models are taken into account and quantified, demonstrating the potential for complexity adaptation of every individual subsystem within entire-system simulations.