Gear shifting multi objective optimization to improve vehicle performance, fuel consumption, and engine emissions
ABSTRACT In the present work, the Adaptive Weight Genetic Algorithm was employed in order to determine the gear shifting strategies that allow an automobile to work in the best compromise among fuel consumption, engine emissions, and vehicle performance. For the assessment of each of the three objective functions, a simulation model based on engine data and on the well established equations of the longitudinal dynamics was developed. The driving cycle chosen for the calculations was the FTP 75, which takes into account both cold and hot starts, meaning that the transient operation during the warm up of the catalyst is also considered.