Particle Swarm Optimization for Control Strategy of Hybrid Electric Vehicles

Abstract

     This paper presents a particle swarm optimization algorithm (PSO) as a control strategy for the offline driving cycle to obtain the best torque distribution between the two sources: internal combustion engine (ICE) and the electric motor (EM). The purpose to minimize the fuel consumption, emissions, and maximize the state of charge of the battery for the model of power-split hybrid electric vehicles (PSHEV), while the requirements of the driving performance considered as constraints. The control strategy has been applied for the UDDS driving cycle under the Matlab Simulink software environment. The results of the value of fuel consumption compared with fuzzy logic control (FLC), the global optimization genetic algorithm (GA), and ADVISOR.  After comparing, the results demonstrate the effectiveness of the (PSO) algorithm over the mentioned methods in lowering fuel consumption by 8.87% for the (FLC),  22.6% for the GA. Maximizing the state of charge of the battery by 5.6% for the ADVISOR program and closest to optimal results for FLC