Application of Particle Swarm Optimization to Design Control Strategy Parameters of Parallel Hybrid Electric Vehicle with Fuel Economy and Low Emission

摘要

Hybrid Electric Vehicles (HEV) provide fuel-saving and less emissions, and are one of the solutions to effectively alleviate the global energy crisis and environmental pollution problems for a large number of global vehicle demands. This study mainly uses Particle Swarm Optimization (PSO) algorithm to design appropriate control strategy parameters. To simulate a parallel hybrid electric vehicle, we used ADVISOR as a simulation tool and used the driving cycle of Federal Test Procedure (FTP) to evaluate fuel consumption (FC), exhaust emissions, and vehicle dynamics. The number of iterations is set to 50, the swarm size is set to 10, the inertia weight is set to 0.8, as well as the cognitive parameter and the social parameter are both set to 2 for the proposed PSO. Compared with ADVISOR the defined parallel HEV, the results show that PSO is a powerful tool for design of parallel HEV control strategy parameters. The proposed method can improve fuel consumption and reduce exhaust emissions without sacrificing vehicle performance. It may help to alleviate the global energy crisis and environmental pollution.