Design Optimization and Optimal Power Control of Fuel Cell Hybrid Electric Vehicles Based on Swarm Intelligence

摘要

The fuel consumption of fuel cell hybrid vehicles depends significantly on the energy control strategy and the component sizing. Therefore, it is necessary to determine the optimal design and power sharing between the sources in order to minimize the fuel consumption and components cost of the vehicle for a given driving cycle. This paper proposes a control strategy based on Particle Swarm Optimization (PSO) in order to achieve the optimal design and minimum fuel consumption for Fuel Cell/Supercapacitor hybrid electric vehicles (FC/SC HEVs). PSO algorithm is a member of the wide category of Swarm Intelligence Methods (SI). To evaluate the performance of the proposed methodology for FC/SC HEVs, a modified PSO algorithm will be compared with Genetic Algorithm (GA) while satisfying the operational constraints. In this research, three control strategies based on the knowledge of the fuel cell efficiency map are implemented and compared with the aim to minimize the hydrogen consumption for the FC/SC HEV. These control strategies are control strategy based on Efficiency Map (CSEM), control strategy based on Particle Swarm Optimization (CSPSO) and control strategy based on Genetic Algorithm (CSGA). To demonstrate the effectiveness of the proposed methodology, simulation studies are performed using Matlab/ Simulink by integrating the detailed mathematical and electrical models of the FC/SC hybrid vehicular system.