Optimization Research Of The Parameters Match Of Transmission System Based On Passant1.8T

There are always many travelers and transportation during human life of economic,political, social and cultural activities. With the development of society, vehicle has becomean indispensable tool of human social activities. Followed, it leaded more and more conflictsbetween energy and environment. Reducing the energy consumption of vehicle andimproving the transport efficiency have become the focus of the current social studies. As thevehicle power transmission system has a great impact on the performance of the power andfuel economy, the study of the matching parameters of the vehicle powertrain has a veryimportant significance.The method that often used to solve the transmission parameter matching optimizationincluded that: fuzzy optimization, penalty function, interval optimization and other computeroptimization algorithm. Those algorithms were optimized by simulated model according tothe vehicle dynamics equation and the indicators of the power performance and fuel economy,So the optimized results have a certain gap with the actual vehicle operating conditions. Inaddition, during the process of simulation and modeling, there are no generally acceptedevaluation indexes of optimal matching and the common practice is to weighted the indicatorsof power performance and fuel economy, it is lead to the evaluation results of matching thepower train parameter have a certain degree of subjectivity.During the study of vehicle powertrain parameters match, this article used VM Passat1.8Tas the basic model, based on the actual test date about the acceleration performance and fuelconsumption that vehicles have the same performance, taken the transmission ratio and themain gear transmission ratio as design variables, then forecasted the optimal parametersmatch with BP neural network simulation. Next, this article based on the dynamic equationsof vehicle, taken the vehicle dynamics indicators and economic indicators as the evaluationindicators, according to the theory of genetic algorithm optimized the parameters of thetransmission ratio and the main gear transmission ratio and obtained the optimizationmatching results. Finally, this article applied AVL-Cruise software to set up the institutions ofthe vehicle model and simulate the performance of the vehicle with train parameters matchfrom BP neural network and the genetic algorithm. By the comparison of theMax.gradeability, maximum acceleration, fuel consumption of each gear transmission, thearticle concluded the advantages and disadvantages about BP neural network predicted optimization and the genetic algorithm optimization, as well as validated the feasibility of thetwo optimization methods.