Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles

Abstract

This paper describes the application of the genetic algorithm for the optimization of the control parameters in parallel hybrid electric vehicles (HEV). The HEV control strategy is the algorithm according to which energy is produced, used, and saved. Therefore, optimal management of the energy components is a key element for the success of a HEV. In this study, based on an electric assist control strategy (EACS), the fitness function is defined so as to minimize the vehicle engine fuel consumption (FC) and emissions. The driving performance requirements are then considered as constraints. In addition, in order to reduce the number of the decision variables, a new approach is used for the battery control parameters. Finally, the optimization process is performed over three different driving cycles including ECE-EUDC, FTP and TEH-CAR. The results from the computer simulation show the effectiveness of the approach and reduction in FC and emissions while ensuring that the vehicle performance is not sacrificed.