New Solution Supporting Efficient Vehicle Calibration Using Objective Driveability Evaluation and AI

In recent years, more and more automobile manufacturers and suppliers have started to focus on the development and practical application of objective driveability evaluation systems. The limited reproducibility and comparability of subjective driveability evaluation is significantly improved by using objective driveability evaluation systems. A widely used system for driveability objectification is AVL-DRIVE. This system calculates a real-time rating, so called AVL-DRIVE rating (DR) for comfort and dynamics in all relevant driving conditions of the vehicle. In addition, physical parameters are calculated, so that developers can quickly gain a deeper understanding of the driveability evaluation and optimize driveability by analyzing the physical parameters and ratings.

For further increase of efficiency in driveability calibration, additional relevant physical parameters and objective evaluations based on xCU signals are calculated in AVL-DRIVE, subsequently, tool-based correlation and optimization studies are carried out with the support of AI (Artificial Intelligence). The aim is to support the calibration engineer in the data analysis to quickly identify abnormal events in the calibration and determine the most likely root cause. This new solution was developed and implemented in cooperation with the Institute of Automotive Engineering (IAE) at the Technical University of Braunschweig and AVL List GmbH.

Particular attention is paid to the linking of poor driveability rating (DR) and underlying settings in the calibration. In particular, the torque intervention, the clutch pressure control, and the speed adjustment during the shift process in automatic and dual clutch transmissions are considered. With the developed AI-based methodology, the efficiency of objectified driveability calibration of vehicles can be increased.