Genetic-fuzzy shifting strategy for continuously variable transmission in parallel HEV

Abstract

This paper describes a methodological approach for optimization of the continuously variable transmission (CVT) shifting strategy in hybrid electric vehicle (HEV). In this approach, a fuzzy-based strategy is employed for the CVT shifting management. The fuzzy membership function parameters are then optimized using the genetic algorithm (GA). In this study, the optimal selection of the fuzzy control parameters is formulated as a constrained optimization problem. In addition, the objective is defined to minimize the vehicle fuel consumption and emissions while satisfying the driving performance constraints. The optimization process is performed over three different driving cycles including TEH-CAR driving cycle. TEH-CAR driving cycle is developed based on the experimental data collection from the real traffic condition. Results from computer simulation show effectiveness of the approach, resulting in reduction of fuel consumption and emissions while ensure that the vehicle performance is not sacrificed.