Adaptive neural network optimisation control of ICE for vehicle with continuously variable transmission

Abstract

Continuously variable transmissions (CVT) have received great interest as viable alternative to discrete ratio transmission in passenger vehicle. It is generally accepted that CVTs have the potential to provide such desirable attributes as: a wider range ratio, good fuel economy, shifting ratio continuously and smoothly and good driveability. With the introduction of continuously variable transmission (CVT), maintaining constant engine speed based on either its optimum control line or maximum engine power characteristic could be made possible. This paper describes the simulation work in drivetrain area carried out by the Drivetrain Research Group (DRG) at the Automotive Development Centre (ADC), Universiti Teknologi Malaysia, Skudai Johor. The drivetrain model is highly non-linear; and it could not be controlled satisfactorily by common linear control strategy such as PID controller. To overcome the problem, the use of adaptive neural network optimisation control (ANNOC) is employed to indirectly control the engine speed by adjusting pulley CVT ratio. In this work, the simulation results of ANNOC into drivetrain model showed that this highly non-linear behaviour could be controlled satisfactorily.