Modelling and Optimization of Toroidal Continuously Variable Transmission in ECE Driving Cycle

Abstract

In the present study, the aim is to optimize full and half toroidal continuously variable transmission (CVT) in order to minimize the vehicle fuel consumption (FC) in ECE driving cycle. First, the model of both CVTs’ efficiency is presented. The control strategy of CVTs speed ratio based on minimizing the vehicle FC is introduced, and the algorithm of calculating the vehicle FC is shown. Afterwards, both types of CVT are optimized using Particle Swarm Optimization method (PSO) with the aim of minimizing the vehicle FC in ECE driving cycle, and the optimized geometries are achieved. It is found that a remarkable fuel economy can be achieved through optimization of both types. The effects of the vehicle weight on the optimized geometries are examined. It will be shown that, the optimized geometry of full toroidal type isn’t strongly influenced by the vehicle weight, while the optimized geometry of half toroidal one varies through variation of the vehicle weight.In the present study, the aim is to optimize full and half toroidal continuously variable transmission (CVT) in order to minimize the vehicle fuel consumption (FC) in ECE driving cycle. First, the model of both CVTs’ efficiency is presented. The control strategy of CVTs speed ratio based on minimizing the vehicle FC is introduced, and the algorithm of calculating the vehicle FC is shown. Afterwards, both types of CVT are optimized using Particle Swarm Optimization method (PSO) with the aim of minimizing the vehicle FC in ECE driving cycle, and the optimized geometries are achieved. It is found that a remarkable fuel economy can be achieved through optimization of both types. The effects of the vehicle weight on the optimized geometries are examined. It will be shown that, the optimized geometry of full toroidal type isn’t strongly influenced by the vehicle weight, while the optimized geometry of half toroidal one varies through variation of the vehicle weight.