Design optimization of a parallel hybrid electric powertrain

Abstract

The design of a hybrid electric vehicle (HEV) involves a number of variables that must be optimized for better fuel economy and vehicle performance. In this paper, global optimization algorithms-DIRECT (Divided RECTangles), simulated annealing, and genetic algorithm are used for the design optimization of a parallel hybrid electric vehicle. Powertrain system analysis toolkit (PSAT) is used as the vehicle simulator for this study. The objective of this study is to increase the overall fuel economy of a parallel HEV on a composite of city and highway driving cycle and to improve the vehicle performance. A hybrid algorithm is also developed and is applied to Rosenbrook's Banana Function for the examination of its efficiency.