Ant colony optimization applied to optimal energy management of fuel cell hybrid electric vehicle

Abstract

This paper investigates the applicability of the Ant colony based algorithms to solve dynamic optimization problems and specially focused on optimal control problems. The algorithm was implemented to solve optimal control of energy management in fuel cell hybrid vehicle (FCHV) as a case study. The main components of this vehicle are battery and fuel cell. The objective is to reach the best performance, fuel economy, and acceptable operation of this hybrid structure. Ant Colony algorithms are used to solve combinatorial problems in recent years, especially in engineering problems. Ant Colony algorithm belongs to a class of search algorithms derived from the process of ant-searching food. An object oriented programming (OOP) tool is developed for simulation of this hybrid structure. The simulation results demonstrate the validity and the convenience of ACO approach for dynamic optimization problem and encourage more research towards other application. It prepares a good environment for benchmark of different supervisory control of FCHV.