Basics of ASCMO-MOCA

ASCMO-MOCA enables optimization of model parameters and minimizes the deviation of model prediction and desired output values.

E.g. modern vehicle ECUs contain physics based models to replace or monitor real sensors. Such a physics based model is generic, but must be adapted to an actual engine. Parameters (maps/curves/scalars) are optimized using real measurements, e.g., from test bench or vehicle.

The model can be represented in ASCMO-MOCA as a set of formulas entered by the user. Alternatively, existing models, e.g. from Simulink®, can be used.

In this chapter, you can find a description of the basic concepts of ASCMO-MOCA.

These are the following:

  • Fields of Application of ASCMO-MOCA

    This section provides a general overview of the wide range of application fields in ASCMO-MOCA.

  • Elements of the ASCMO-MOCA User Interface

    This section provides an brief overview of the user interface key elements of ASCMO-MOCA.

  • Data

    This section provides information on import, analysis and preprocessing of measured data.

  • Assessment of the Input Data

    In this section you will find information on how you can assess the quality of the input data used by ASCMO-MOCA for the parameter optimization.

  • Models

    This section provides information on importing and using external models in ASCMO-MOCA.

  • Function

    This section provides information on how to create a model by specifying a set of formulas that form a function.

  • Parameters

    This section contains general information about the optimization of parameters within ASCMO-MOCA.

  • Available Types of Parameters

    This section provides a brief overview of the various types of parameters that can be used in the function (see Step 5: Build Up the Function) for optimization (see Step 6: Optimization).

  • Optimization

    This section contains a description of the different optimization methods and the optimization criteria that can be used for the parameter optimization.