Tutorial: Working with ASCMO-STATIC

This chapter introduces the basic functions of ASCMO-STATIC by means of an example.

This tutorial is structured as follows:

• Inputs and Outputs of the Measured Engine

  This section provides information about the inputs and outputs of the measured engine and the measured data used.

• Before the Model Training

  This part of the tutorial describes how to start ASCMO-STATIC and how to evaluate and improve the quality of the training data set. Thus, you can raise the quality of the trained model.

• Model Training

  This part of the tutorial describes the direct path from reading the data to the model training

• Model Improvement

  This part of the tutorial describes how to assess and improve the trained model.

• Visualizing

  The treatment of this section is not absolutely required for the further sequence of the tutorial. However, it is useful to familiarize oneself with the visualization options of ASCMO-STATIC.

• Optimization

  In this part of the tutorial, you perform several types of optimization with ASCMO-STATIC. You also learn how to work with the results of a global optimization at several operating points.

• Driving Cycle Forecast

  This section provides information about how to use a driving cycle for the definition of the prognosis calculation rules in ASCMO-STATIC.

• Cycle-Based Global Optimization

  This section of the tutorial shows how to perform cycle-based global optimization with ASCMO-STATIC, using the model of a diesel engine as an example.

• Model Export

  In this section you will learn how to export the ASCMO-STATIC models to MATLAB®, INCA/MDA, Python, Simulink®, Excel, C code, GT‑Suite or FMI.