Optimization of Component Sizes in Parallel Hybrid Electric Vehicles via Genetic Algorithms

Abstract

This paper describes the optimization of the parallel hybrid electric vehicle (HEV) component sizing using a genetic algorithm approach. The optimization process is performed over three different driving cycles including the European ECE-EUDC, American FTP and TEH-CAR cycles in order to investigate the influence of the driving pattern on the optimal HEV component sizes. Hybrid Electric Vehicles are considered as a solution to the world’s need for cleaner and more fuel-efficient vehicles. HEVs use a combination of an internal combustion engine and an electric motor to propel the vehicle. Proper execution of a successful HEV design requires optimal sizing of its key mechanical and electrical components. In this paper, genetic algorithm is used as the optimization approach to find the best size of internal combustion engine, electric motor and energy storage system. The objective is minimization of fuel consumption and emissions while vehicle performances, like acceleration and gradeability are defined as constraints. These constraints are handled using penalty functions. Simulation results reveal that the HEV optimal component sizing is independent from the driving pattern. However, the amount of fuel use and emissions are extremely dependent on the driving cycles. In addition, the results show, while the performance constraints are within the standard criteria, the reduction in fuel consumption and emissions are achieved.