Segmented Tracks Planning of Roadway-Powered System for Electric Vehicles using Improved Particle Swarm Optimization

摘要

As a kind of prospective green vehicles, electric vehicles have not been welcomed by potential customers due to drawbacks such as the high price, short driving range and long charging time. The Roadway-powered Electric Vehicles (RPEVs) using an Inductive Power Transfer (IPT) is considered as an effective solution to resolve these drawbacks. In the segmented RPEVs system, efficiency and annual cost are affected by many factors, such as the track distance, tracks interval, number of tracks and installed capacity of each track. According to such problem, the Nonlinear Programming (NLP) model for segmented tracks planning of RPEVs system is proposed in this paper. An Improved Particle Swarm Optimization (IPSO) algorithm is adopted to solve the proposed NLP model to minimize the annual cost. A case for segmented tracks planning is designed to test the rationality of the proposed NLP model and the performance of the IPSO algorithm. Simulation results show that the IPSO algorithm is more accurate, consistent and effective than the classical PSO algorithm.