基于遗传算法的轻度混合动力汽车动力传动系参数优化  

Powertrain Parametric Optimization of Mild Hybrid Electric Vehicle Based on Genetic Algorithm

作　　者：杜子学[1] 查雷 

机构地区：[1]重庆交通大学机电与汽车工程学院,重庆400074

出　　处：《汽车工程学报》2013年第6期433-439,共7页Chinese Journal of Automotive Engineering

基　　金：重庆市科技计划项目(CSTC2011GGC250)

摘　　要：在研究某ISG型轻度混合动力汽车(Mild Hybrid Electric Vehicle,MHV)控制策略的基础上,选择对动力性和经济性有较大影响的主减速器传动比、变速器各挡传动比以及变速器换挡规律为优化参数,以加速时间和等效燃油消耗为优化目标,通过GT-SUITEMP建立整车仿真模型。采用带精英策略的快速非支配排序遗传算法(NSGA-II),基于mode FRONTIER(MF)建立了多目标优化模型。通过联合仿真分析,得到了Pareto最优解,与优化前相比,加速时间减少了3.5%,等效燃油消耗降低了9.4%。Based on the study of the control strategy of integrated starter/generator mild hybrid electric vehicle （MHV）, the vehicle simulation model was established via GT-SUITEMP. Aiming at optimizing acceleration time and equivalent fuel consumption, the final drive ratio, each gear ratio and gear shifting rule were selected as optimization parameters, which have great influence on power performance and fuel economy. Based on the mode FRONTIER （MF）, multi-objective optimization model was set up by adopting the fast and elitist non-dominated sorting generic algorithm （NSGA-II） and the Pareto optimal solutions could be obtained through co-simulation analysis. Compared with the original vehicle, acceleration time is reduced by 3.5% and the equivalent fuel consumption is reduced by 9.4% in the final solution.

关 键 词：轻度混合动力汽车(MHV) 动力传动系 遗传算法 多目标优化 

分 类 号：U464.3[机械工程—车辆工程]