Walters, T., Shaw, P., Madurai Kumar, M., and Hoop, J., "Analysis Lead Drivability Assessment," SAE Technical Paper 2015-01-2804, 2015, doi:10.4271/2015-01-2804.
Drivability and powertrain refinement continue to gain importance in the assessment of overall vehicle quality. This notion has transcended its light duty origins and is beginning to gain considerable traction in the medium and heavy duty markets. However, with drivability assessment and refinement also comes the high costs associated with vehicle testing, including items such as test facilities, prototype component evaluation, fuel and human resources. Taking all of this into account, any and all measures must be used to reduce the cost of drivability evaluation and powertrain refinement.This paper describes an analysis based co-simulation methodology, where sophisticated powertrain simulation and objective drivability evaluation tools can be used to predict vehicle drivability. A fast running GT power engine model combined with simplified controls representation in Matlab/Simulink was used to predict engine transients and responses. This high fidelity engine model was coupled with a detailed vehicle model in an AVL CRUISE™ environment that included dynamic models of the various driveline elements. With such a detailed vehicle model, specific driving scenarios and conditions were simulated and the responses were passed to AVL DRIVE™ for drivability evaluation. AVL DRIVE is a drivability evaluation tool comprising of a data collection system and data analysis software that can recognize and score individual drivability events. Within Drive the collected objective data was compared to a database of best in class vehicles and a subjective driveability score based on a 1-10 scale was returned. The results of this initial drivability simulation were then used to determine needed improvements to the powertrain calibration and control strategy. While this study is a preliminary step towards using analytical models for driveability assessment, greater refinement of the models is necessary in order to achieve better correlation with the experimental data. Such a methodology can tremendously reduce the amount of time and effort required to understand and tune drivability, before on-road testing commences.