Automating Shift-Scheduling Calibration by Using Bionic Optimization and Personalized Driver Models

Abstract

Shift-scheduling calibration is important to the automobile industry, but it is repetitive and time-consuming; it is thus desirable to have a robot driver to automate this process. In this paper, we propose automating the calibration of shift-scheduling by using bionic optimization, i.e., particle swarm optimization (PSO), to guide the searching process and to integrate the driving styles into the calibration by equipping the robot drivers with personalized driver models. The personalized driver model is established by imitating the human driving behavior and is employed as a robot driver to conduct the driving cycle test, i.e., FTP-72 or US06, for candidate shifting schedules. The shifting performance is evaluated online via the computed performance index and/or AVL-Driver, regarding both driveability and fuel economy. Guided by PSO, candidate schedules are generated, tried, and evaluated until an optimal or near-optimal solution is obtained through iterations. Numerical experiments are presented to verify the feasibility and effectiveness of the proposed scheme. The shifting performance is improved by about 2% in computed performance index when compared with the base map. It is also suggested that personalized calibration is preferred if economically feasible.