Automated calibration of gearshift controllers using iterative learning control for hybrid systems

摘要

Newer-generation automatic transmissions are typically characterized by large ratio spreads and small ratio steps, which enable efficient engine operation and good gearshift comfort that is essential for vehicle drivability. However, the increased number of gear ratios results in an exponential increase in the number of gearshift control parameters to be calibrated. Existing automated calibration approaches using dynamometers for testing are Design-of-Experiments (DoE) based and, consequently, require a large number of gearshifts for the calibration of gearshift controllers. The full potential of automation of the calibration process is still to be realized. With this underlying motivation, a model-based automated calibration algorithm that uses at its core Iterative Learning Control (ILC) for hybrid systems is presented in this study. A recently developed theory of ILC for hybrid systems is adapted for the application of gearshift control. Three design methods for the computation of learning controllers for automated calibration of gearshift controllers are presented, and validated numerically using realistic computer simulations of 1-2 power-on upshifts and experimentally via dynamometer testing of 2-3 power-on upshifts. The results demonstrate the ability of the developed ILC procedures to perform automated calibration of gearshift controllers with far fewer gearshifts than state-of-the-art automated calibration approaches.