Optimal Control Strategy Parameters of Parallel Hybrid Electric Vehicles Based on Particle Swarm Optimization

Abstract

Hybrid Electric Vehicles (HEVs) allow fuel economy and reduced emissions in comparison to conventional vehicles. To improve HEV performance in relation to reduce fuel utilization and emissions, and guarantee driving performance, the optimization of control strategy is indispensable. In this paper, the multiobjective optimization problem is converted to single-objective problem. Particle Swarm Optimization (PSO) algorithm is then used to conceive appropriate control parameters, for the purpose to reduce fuel consumption and emissions with conserved vehicle performance requirements. To simulate a parallel hybrid electric vehicle, ADvanced VehIcle SimulatOR (ADVISOR) is used with Federal Test Procedure (FTP) and Urban Dynamometer Driving Schedule (UDDS) to estimate Fuel Consumption (FC), emissions and vehicle dynamics.