ASCMO-DYNAMIC

ETAS ASCMO-DYNAMIC enables you to create data-based models that capture the dynamic/transient behavior of complex systems. ASCMO-DYNAMIC offers a wide range of functions and options for visualizing and analyzing system behavior. Additionally, it allows for exporting to ASCMO-MOCA for optimizations and supports the creation of experimental designs based on the DoE methodology (design of experiments).

Using AI methods from the field of machine learning, ASCMO-DYNAMIC allows you to accurately model complex relationships without requiring precise knowledge of the underlying algorithms. This flexibility makes the software suitable for a wide range of users, including less experienced users who appreciate parameter-free, automated model creation, as well as experts who benefit from extensive configuration options.

A typical application of ASCMO-DYNAMIC is the modeling of transient processes in internal combustion engines, particularly in the context of Real Driving Emissions (RDE), where purely stationary consideration of emissions and fuel consumption may not be sufficient. Dynamic effects, such as peaks, can significantly influence the overall outcome. When modeling relevant variables like fuel consumption and pollutant emissions, historical values and the rate of change are taken into account, enabling a detailed, time-resolved analysis of the influence of dynamic driving maneuvers on the relevant output variables.

The methodology used in ASCMO-DYNAMIC is not limited to the internal combustion engine, making the tool applicable in areas such as electric mobility (e.g., battery modeling).