Research On Heavy-Duty Vehicle Emission Prediction Modeling And Emission Reduction Countermeasures

The exhausted pollution controlling of heavy-duty diesel vehicles is crucial to improving the human environment.In China,with the attention paid to the pollution of automotive vehicles,a great deal of research has been conducted on vehicle emissions testing and emission models.However,the limitations of data collection make the domestic research mainly based on the application of foreign models.Due to different national conditions,the driving conditions abroad cannot reflect the actual road emission characteristics of China.Therefore,there is an urgent need to build an emission model that reflects China's actual road characteristics,which can provide basic data support for heavy diesel vehicle emissions,and make further development of pollution controlling strategies.In this paper,the following tasks have been completed for the establishment of vehicle exhausted gas measurement and emission prediction models:Firstly,the object of the study is a heavy-duty diesel vehicle with the National IV emission standard.It introduced the basic principles of on-road vehicle exhaust emission test,designed the vehicle exhaust emission test scheme,Based on the PEMS,the exhaust emission test of the heavy-duty diesel vehicle was carried out,and the per second emission rate of four pollutants was obtained.It analyzed the existing problem in collected data,put forward the processing plan of wrong data.Secondly,according to the actual exhaust emission data obtained from the emission test,the factors affecting the emission pollution of the vehicle are analyzed.The influence of the environmental temperature,humidity and road type on the emission is excluded.The effect of the vehicle running mode on the emission is analyzed,and the highest emission rate of the acceleration is found.The effects of velocity,acceleration and VSP on emission are described in detail.Thirdly,VSP and instantaneous speed V are selected as the parameters to divide the driving range,and the neural network is used to establish the neural network model with the four pollutants per second emission rate as the model output.The relative error of the neural network model is 17.3% and the minimum is 10%.In order to improve the convergence effect of the neural network model is not ideal and the model error is large,the regression analysis is carried out after the discharge data is segmented,and the relative error of the regression analysis model is 9.28%.The results show that the regression analysis model has better prediction accuracy and practicability.Finally,combined with the application of emission reduction technology,the emission of nitrogen oxides from major pollutants discharged from heavy-duty vehicles was predicted,and the corresponding reduction measures were made according to the effect of emission reduction.