Webinar: Optimization of Model Parameters with ETAS ASCMO-MOCA

The focus of this webinar is to show the basics of efficient optimization of parameters in physics based models such as those used in the ECU and simulation environment. We will explain how the simple and intuitive operation of the ETAS ASCMO-MOCA tool can be used to efficiently perform even complex optimization tasks.

Powerful algorithms are able to optimize a large number of free parameters simultaneously, taking into account constraints such as smoothness or monotonicity.

ASCMO-MOCA is often used to optimize the predictive quality of ECU models (virtual sensors) for e. g. torque or exhaust gas temperature. This means that the deviation of the model prediction compared to a real measurement from the engine test bench or the vehicle for all measurement points is minimized.

Since the methodology used in ASCMO-MOCA is universally applicable, applications can be found not only in the environment of combustion engines, but also in areas such as e-mobility (e. g. charging strategy) and component development.