Basics

ETAS ASCMO-MOCA enables the optimization of parameters in physics-based models, such as those used in the ECU and simulation environments. Various plant models and controller models can be loaded, connected, or modeled for this purpose. It is also possible to load measurement data, import and export model parameters, and define optimization tasks. ASCMO-MOCA provides a wide range of functions and options for visualizing and analyzing the data, as well as the models used. Powerful algorithms can optimize a large number of free parameters simultaneously while considering constraints such as smoothness or monotonicity.

A common use for ASCMO-MOCA is in optimizing the prediction quality of ECU models (virtual sensors) for e.g., torque or exhaust-gas temperature, minimizing the deviation of the model prediction from real measurements on the engine test bench or in the vehicle for all measuring points.

Another application is optimizing emissions and fuel consumption for complex internal combustion engines in dynamic/transient driving cycles. This requires linking classic ASCMO data-based models to parts of the ECU software. Such linkages and the joint optimization of different subcomponents are straightforward in ASCMO-MOCA.

Since the methodology used in ASCMO-MOCA is not limited to the internal combustion engine, the tool is also used in areas such as electric mobility (e.g., charging strategy) and component development.