Design and off-line tuning of a longitudinal driver model for EiL applications

Abstract

Tightnening emission regulations and increasing powertrain complexity lead car manufacturers to rely on novel testing methods in order to frontload development. Among these, Engine-in-the-Loop, that is, the coupling of a physical internal combustion engine (ICE) on a testbed with a virtual environment, shows great promise for emission- and consumption-related tasks. In particular, this study focuses on the driver model, a simple yet crucial component of the virtual environment. A longitudinal driver model is developed in Simulink based on the PI-regulation structure and augmented with anti-windup, cycle preview, and takeoff strategy. While the PI approach is generally chosen in the literature, this study details the implementation of the added functions, and proposes a method for the gains of the model to be tuned in simulation by considering engine dynamics, and using several performance indicators. The virtual driver is then tested in a complete EiL setup simulating an electric hybrid driveline and shows satisfactory cycle-following and overall behavior on a WLTC. Robustness of the tuning method is also studied by varying vehicle parameters on the EiL bench.