Modern ECUs typically contain many physically based models represented by a complex structure of maps, curves and scalar parameters. The task of these models is to monitor or predict engine values that are normally measured by real sensors. If the quality of the model structure is good enough and the parameters are well fitted, such a model can replace the sensor–and serve as a virtual sensor. Among others, virtual sensors are commonly used on the ECU for predicting engine torque, air pressure/flow, emissions, catalyst and exhaust gas temperatures.
To ensure optimal prediction quality of these models, their parameters need to be calibrated appropriately using real measurement data collected, e.g., in the vehicle or at the test bed. Due to the complexity and the high number of parameters, manual calibration is very time consuming and often not efficient or even impossible.
The solution is ETAS ASCMO-MOCA (MOdel CAlibration). ASCMO-MOCA allows the user to perform the parameter optimization in a few easy steps through an intuitive graphical user interface:
- Load measurements and existing calibration data (optional)
- Analyze and visualize all data
- Load and link existing Simulink® models, or create formulas to represent the structure of the model/function
- Define the optimization task and start the automated optimization
- Upon completion, the results can be visualized, validated and exported
The procedure is exactly the same for any optimization task, which means that even completely different optimizations for different ECU functions/models can be done in the same tool without the need to learn a new procedure.
If you struggle with the ever increasing complexity of ECU functions or want to perform some pre-calibration earlier in the development cycle even before ECU software is available, then ASCMO-MOCA might be the right tool. Comment below with questions or contact us at firstname.lastname@example.org for more information. Our experts can help analyze your particular challenge and get you started with an evaluation of the tool.