Energy management in hybrid electric vehicles using particle swarm optimization method

Abstract

Increasing level of environmental pollution, petroleum prices and depleting level of natural resources are major troubles caused by internal combustion engine based transportation system. Hybrid electric vehicles (HEVs) have presented the solution to these problems and are assumed to be future green and sustainable transport medium. HEVs utilizes engine and battery together to give power to the wheels. Since, presence of two sources causes the complexity at architectural level of vehicle, hence requires a judicious power split between them. To split power efficiently between engine and battery, an intelligent energy management scheme is required to be implemented. An efficient power split scheme may consequence in better fuel economy and performance of HEVs. Here, particle swarm optimization based intelligent energy management scheme is implemented and compared with genetic algorithm and dividing rectangle algorithms. Modified state of charge (SOC) estimation method and 1RC battery model are used for simulation purposes in advanced vehicle simulator (ADVISOR).