基于粒子群算法的汽车传动系参数优化匹配分析

Optimized Matching and Analysis of Vehicle Power Train Based on PSO

分别建立整车的动力性目标函数和经济性目标函数,对动力性和经济性目标函数赋予不同的加权因子,然后对其进行归一化处理,得到以汽车传动比为优化变量的目标函数。利用基于微粒间互相学习搜索最优区域的粒子群算法进行寻优计算,获得最优的传动比参数。以此建立某一乘用车的Cruise整车仿真模型,制定Cycle Run、Climbing Performance以及Full Load Acceleration计算任务,对比仿真分析优化前后整车的油耗、爬坡性及全负荷加速性。仿真结果表明:汽车的整车动力和经济性有明显改善。

The objective function of vehicle power and economic were established respectively and the separate weighting factors were assigned to the power function and economic function and then normalization processing was carried out, and the objective function based on automotive transmission ratio as optimization variables was established. The PSO based on inter-atom searching for optimal region to learn from each other was used to optimize calculation, and the optimum transmission ratio was obtained. The Cruise vehicle simulation model of a passenger car was established, and the computing tasks of the Cycle Run, Climbing Performance and Full Load Acceleration were formulated. The vehicle＇s fuel consumption, climbing performance and full load acceleration were compared with the simulation analysis before and after optimization. The simulation results show that the vehicle＇s power and economic performance has a great improvement.