The subject of research is systems with strongly varying degrees of maturity, which can range from “observation and description of the functional principle” (Technology Readiness Level, TRL = 1) to “prototype in use” (TRL = 7). Typically, simulation tools are increasingly used for technologies with low maturity levels. With increasing maturity, experimental methods, e.g. as software or hardware-in-the-loop (SiL and HiL), are integrated into the development. In order to be able to investigate system behavior under real operating conditions, we participate in projects with strong practical and market relevance, such as real laboratories and prototype testing.
For an early simulation of technical systems, a suitable modeling is essential. Here physical models are used, which can usually be assigned to the group of white-box models. In applications with a higher degree of maturity there is usually already a lot of information available about the system itself and related systems, which makes a complete explicit modelling difficult or very costly. Grey or black box models can be used in this case, which work partially or completely without physical modeling. Artificial intelligence (AI) approaches also offer great potential. These models can be used, for example, in so-called “digital twins”.
HiL setups are potent approaches for further testing of hardware and software with a medium degree of maturity. Not all system elements have to be developed to the same level of maturity to enable testing. For example, new vehicle drive concepts can be tested on the Car-in-the-Loop test bench without having to implement them in a real vehicle.
Many challenges of technical systems only arise in later operation under real operating conditions. Therefore, we often strive for testing under real conditions. With the data obtained in this way, for example, parameters of grey and black box models can be identified, or entire simulation environments can be validated. Our focus lies on functional testing and less on endurance testing.
Due to this broad spectrum from virtual simulation to physical testing, IMS is able to accompany innovations and obtain relevant research results throughout the entire development cycle.