2 About ETAS ASCMO | 11
2.1.2 Model-Based Calibration
The calibration of ECUs becomes increasingly more complex and expensive due
to several factors.
The main causes are:
increasing variety of variants of a base version
decreasing availability of test objects (engine, vehicle)
increasingly stricter requirements concerning the consumption, emissions
and diagnostics.
This increasing complexity of the task can no longer be handled using "classical"
methods of calibration. Even if automating routine processes, a series of tasks
must be iteratively executed:
Measurement and variation of ECU parameters
Measurement of responses of the experimental vehicle/engine
Analysis of the measurement
Step-by-step optimization
The whole procedure leads to one optimal data set.
For the model-based calibration with ASCMO-STATIC or ASCMO-DYNAMIC, one
measurement on the real system is sufficient (after creating the experiment
plan). Everything else is performed on the model:
After specifying the optimization targets: One optimization run leads to an
optimal parameter set
Maps can be changed and the resulting behavior can be predicted
Depending on the specification, an optimal result is achieved:
lSmall vehicle: consumption; sports car: torque
lSporty driving behavior (torque is quickly available) vs. comfort
In this case, n iterations result in n optimal data sets.
Example
An example for the model training and optimization of a direct injection engine is
located in 6 "Tutorial: Working with ASCMO-STATIC" on page69. A series of maps
are optimized here with respect to fuel consumption, engine roughness, soot
and NOx emission.
2.2 Basics of ASCMO-DYNAMIC –Overview
Today's model-based calibration tools consider solely steady-state engine beha-
vior in modeling and cannot represent any dynamic effects in predictions. This,
however, is insufficient for simulating dynamic engine processes with time-
dependent or transient relationships, for instance, the engine air system or
exhaust gas emissions.
ASCMO-STATIC V5.11 | User Guide