Future demands regarding emissions, fuel consumption and driveability lead to complex engine and power train control systems. The calibration of the increasing number of free parameters in the ECU's contradicts the demand for reduced time in the power train development cycle.
This paper will focus on the automatic, unmanned closed loop optimization of driveability quality on a high dynamic engine test bed. The collaboration of three advanced methods will be presented:
Objective real time driveability assessment, to predict the expected feelings of the buyers of the car
Automatic computer assisted variation of ECU parameters on the basis of statistical methods like design of experiments (DoE). Thus data are measured in an automated process allowing an optimization based on models (e.g. neural networks).
A reproducible simulation of a real vehicle behavior, including power train and driver action simulation on a high dynamic engine test bed is used as well as special test runs for the automatic driveability calibration.
Thus by utilizing this driveability model it will be possible, to produce market oriented calibrations by simply changing the target parameters.
The current practice for optimizing driveability through ECU calibration, is an empirical process. It is performed utilizing complete vehicles on the road.
In this paper an improved methodology will be presented. The ECU-parameters will be optimized with regard to the customer specific driveability demands within a traceable process.