| As China’s energy shortage and environmental problems have become more and more serious, new energy vehicles will be the automotive industry’s development focus. In addition to the government’s strong support, the development of new energy vehicles become more thriving. Pure electric vehicles, due to a true zero-emission, low-cost products and constantly being developed by the national policy supporting, has been promoted by the auto companies and recognized by consumers. But the electric vehicle power system parameters directly affect the rationality of the car’s power and economy, at the same time determines the ov erall cost of the car, and these factors are consumers concerned about the content, thus affecting the popularity of pure electric vehicles. Therefore, a reasonable choice of power system parameters throughout the development cycle is critical.This paper, based on a pure electric vehicle researching platform of Geely, studied the dynamic system parameter matching process. Driven by national policy and corporatation development needs,Geely Automobile R & D design and produce a pure electric car based on a traditional car. Firstly, according to the target performance, dynamic system parameter is calculated with reference to the theoretical basics of power system. With the parameters matched by theoretical calculations to select the desired power matching system components, those parts includes the drive motor system, battery power system and transmission system. Secondly, to create out every component models based on the selected parts and outline of vehicle the in simulation software GT-suite. We can simulate the vehicle performance in GT-suite. At the same time, for this vehicle prototype,MUCAR,dynamometer experiment was taken out,just to be used to verify the performance of the vehicle and the correction simulation results.Portrait a mathematical model car mechanics to obtain the objective function of dynamic system parameters. We propose a new optimization Thought, that to edit the file in the MATLAB M Chaos particle swarm optimization algorithm to optimize this objective function, and the optimization parameters are drawn into the GT-suite of vhehical simulation model after optimization performance results. By comparing the simulation parameters results with the original design, Chaos particle swarm optimization algorithm can be used to optimize the pure electric vehicle dynamic system parameters, and can draw the desired performance. |
