Study On Vehicle Driving Cycle And Emission Characteristics In Urumqi City

With the continuous development of China's economy,the car ownership is also increasing,traffic congestion and vehicle emissions pollute the environment and other issues are more serious.Therefore,the study of driving conditions and emissions is of great significance to control and improve vehicle emissions.Establishing an accurate and reasonable working condition is the basis of establishing an accurate and realistic emission standard,and a reasonable emission prediction model is of great value for the certification of vehicle pollutant emissions and fuel economy.This paper conduct research on the vehicle driving cycle and emissions in Urumqi.A total of 7 million actual driving data of 10 light vehicles and 2 heavy-duty vehicles were collected randomly from 80 vehicles within 12 consecutive months,and the driving cycle and emissions were studied.The specific research contents are as follows:(1)Intended to the limitation and imperfection in the traditional method of standard driving cycle construction,a driving cycle construction methodology of city road based on optimized projection pursuit and fuzzy clustering algorithm is proposed.A large sample of 12 months' driving condition data of 10 Urumqi light vehicles was obtained by actual measurement,with the kinematic fragment theory,reasonable characteristic parameters were established.Compressing the characteristic parameters of micro-trop based on a cooperation particle swarm optimized projection pursuit model.Then fuzzy clustering the compressed characteristic values,and representative segments were chosen from each category according to the duration percentage.The representative driving cycle of urban light vehicle in Urumqi was established.(2)A diesel exhaust emission model of vehicle based on speed and vehicle specific power is proposed in this paper.After constructing the vehicle driving cycle,a clustering algorithm based on real-time vehicle specific power and emission-sharing rate is used to classify the similar driving states.The relationship between the average emission rate and driving parameters is established by the least-squares regression fitting,thus the micro emission model of the driving parameters and emissions of thevehicle is established.The modeling method proposed in this paper can predict emissions preferably.(3)In view of the complexity and low precision of transient prediction models of vehicle emission,a prediction model based on full-parameter continued fraction is proposed.The particle swarm optimized projection pursuit algorithm is used to compress the parameters of the measured parameters of moving vehicle.Thus,the dimension-reduced data is used as the independent variable of continued fractions.The parameters of the full-parameter continuous model are optimized by particle swarm optimization,then the function of parameters and vehicle instantaneous emission can be established,thus the instantaneous emission prediction of vehicle based on traffic parameters is realized.In addition,the chaos of emission time series is verified,then the emission time series can be predicted in short term through the full-parameter continued fraction model.