基于AVL CRUISE的纯电动汽车仿真与优化OA

Aiming at the multi-objective optimization challenge in parameter matching of power systems for new energy vehicles,especially the engineering pain point that driving range,energy economy and dynamic performance are difficult to be improved synergistically,a parameter collaborative optimization method is studied based on the theory of multidisciplinary collaborative optimization,and a parameter collaborative optimization method integrating system simulation and intelligent algorithms is proposed.Meanwhile,a multi-dimensional parametric model incorporating motor external characteristics,battery discharge characteristics and transmission efficiency is established.A pure electric vehicle is taken as the research object.Based on the vehicle performance index requirements,the preliminary matching of key parameters of the power system is completed.Then,by constructing CRUISE simulation model,combined with the multi-objective genetic algorithm(NSGA-II),with the NEDC cycle driving range,100-kilometer power consumption,and 0～100 km/h acceleration time as the optimization objectives,a collaborative optimization framework including motor peak power,battery capacity and reducer speed ratio is established,and the Pareto optimal solution is obtained by iterative algorithm.The simulation results show that the driving range of NEDC is increased from 357.3 km to 375 km by 4.9%.The 0～100 km/h acceleration time is reduced from 10.93 s to 10.01 s,and the performance is improved by 8.4%.This research verifies the technical advantages of the combination of CRUISE and multi-objective optimization algorithm through quantitative analysis,and the parameter matching method formed can provide theoretical support for engineering practice.