Signal Generator for Prediction of Transient Control Signals of an Automotive Transmission Control Unit Depending on Scalar Calibration Parameters

Event
SAE 2016 International Powertrains, Fuels & Lubricants Meeting
Authors Abstract
Content
In this investigation an innovative signal generator will be introduced, which enables the generation of transient control signals for the gearshift process. The signals are generated merely depending on scalar transmission control unit (TCU) calibration parameters. The signal generator replaces the comprehensive TCU software within the simulation environment. Thus no extensive residual bus simulation is required.
Multiple experimental models represent the core part of the signal generator. To predict the system behavior of the underlying system, the models are trained using measured data from a powertrain with automatic transmission mounted on a test rig.
The results demonstrate that the introduced signal generator is suitable to predict transient control signals for the gearshift operation accurately. In combination with an additional powertrain model it is possible to simulate the gearshift process and subsequently to evaluate the gearshift comfort. The signal generator allows a rapid implementation of a simulation environment for TCU calibration with less modeling effort in comparison with the implementation of the original TCU software. Due to a flexible approach, the signal generator offers to predict signals with any course. Hence, it also allows to predict gearshift-comfort relevant signals directly without using a powertrain model. In conclusion the introduced signal generator emphasizes an universal tool with high potential for model based calibration.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-2155
Citation
Rot, I., and Rinderknecht, S., "Signal Generator for Prediction of Transient Control Signals of an Automotive Transmission Control Unit Depending on Scalar Calibration Parameters," SAE Int. J. Passeng. Cars - Mech. Syst. 10(1):43-53, 2017, https://doi.org/10.4271/2016-01-2155.
Additional Details
Publisher
Published
Oct 17, 2016
Product Code
2016-01-2155
Content Type
Journal Article
Language
English