%0 Journal Article
%T 应用多目标粒子群算法的车辆传动系参数优化仿真研究<br>Parameter Optimization and Simulation System of Vehicle Transmission System Using Multi-objective Particle Swarm Optimization Algorithm
%A 郭谨玮
%A 刘昱
%A 徐月云
%A 王金刚
%A 李孟良
%A 贺可勋
%J 机械科学与技术
%D 2018
%X 利用多目标粒子群算法研究了车辆传动系参数优化问题。首先根据目标车辆的结构搭建了整车模型，通过台架试验对整车模型进行验证；其次以车辆传动系参数作为设计变量，以车辆动力性和经济性作为优化目标，建立多目标优化模型；然后运用多目标粒子群算法对传动系参数进行优化计算，得到Pareto最优解集；最后运用基于信息熵的多属性决策方法，确定了最优传动系参数。研究结果表明：优化后的整车的动力性能和燃油经济性均得到提升，其中燃油经济性提高了4.83%，全负荷0~100 km/h加速性能提升了2.03%。<br>In this paper, the multi-objective particle swarm optimization (MOPSO) algorithm was used to optimized the transmission system parameters of vehicle. Firstly, according to the structure of the target vehicle, the whole vehicle model is built and validated by vehicle bench experiment. Secondly, the vehicle powertrain parameters are taken as design variables and the vehicle power and economy are taken as the optimization targets, a multi-objective optimization model of the vehicle is built. The transmission parameters are optimized by the multi-objective particle swarm algorithm. the Pareto optimal solution set is obtained by calculating the optimization parameters. Finally, the optimal transmission parameters are determined by the multiple attribute decision making method based on information entropy. Finally, the optimal transmission parameters were determined by the multiple attribute decision making method based on information entropy. The results show that the fuel economy is improved by 4.83%, and the acceleration performance of the full load 0~100 km/h is increased by 2.03%
%K 车辆传动系统
%K 多目标优化
%K 多目标粒子群算法
%K 熵
%K 多重属性决策&lt
%K br&gt
%K vehicle transmission system
%K multi-objective optimization
%K particle swarm optimization(PSO)
%K entropy
%K multiple attribute decision making
%U http://journals.nwpu.edu.cn/jxkxyjs/CN/abstract/abstract7000.shtml