Research On Diesel Particulate Filter Regeneration Startup Based On Driving Conditions Recognition

Using diesel particulate trap filter(DPF)to reduce particulate matter(PM)in diesel exhaust emissions and to meet the requirements of emission regulations on PM emission limits is the most effective and reliable technology.The process of removing particulate matter in the DPF is called DPF regeneration.There are many kinds of DPF regeneration.In this paper,the startup of active regeneration of the DPF was determined by means of the diesel engine oxidation catalyst(DOC)assisted DPF regeneration in the Euro VI technical route.DPF's active regeneration should be thorough to achieve low exhaust backpressure to ensure efficient operation of the diesel engine and this requires the engine to run under the right conditions.The actual driving conditions of the car are closely related to the operating conditions of the engine.Therefore,the timing of the active regeneration of the DPF needs to refer to the actual driving conditions of the automobile to determine the choice.This article takes a certain type of truck loaded with heavy-duty diesel engines as a research object.By analyzing the temperature of the car after the turbine and DOC inlet under typical driving conditions,we can determine the timing for active regeneration of DPF in each typical driving condition.Firstly,the basic idea of vehicle driving condition identification strategy is proposed.Based on Matlab/Simulink software,the vehicle driving conditions identification strategy was realized as a model.By analyzing the performance of heavyduty trucks,the characteristics of freight transport,and the characteristics of road conditions,this work determined the typical vehicle driving conditions was used in this article.By analyzing the characteristics of each driving condition,this paper can determine 12 kinds of characteristic parameters required for identifying driving conditions,and the extraction rules for each characteristic parameter.This paper used Matlab/Simulink software to establish the characteristic parameters extraction model and used BP neural network to identify vehicle drivingconditions,designed BP neural network to meet the accuracy requirement.As a result,the BP neural network that satisfies the requirements was presented as a model in the Matlab/Simulink software.This paper completed vehicle driving conditions identification model.The vehicle driving condition identification model can recognize the driving characteristics of the vehicle in real time as a typical driving condition determined in advance.Vehicle driving conditions identification model provided driving conditions information for active regeneration of the DPF.Using AVL Cruise M software to build the dynamic model of heavy-duty truck engine and vehicle,engine bench test selected is the 13 operating point points used in the WHSC test cycle.The engine model calculates the exhaust temperature at the front of the turbine and the inlet of the DOC at each operating point and the exhaust gas mass flow.Compared with data obtained from bench experiments,the accuracy of the engine model was verified.The vehicle dynamics model calculated the maximum grade,maximum speed and 0-50 km/h acceleration time of the car under full load.Comparison of simulation results with data provided by the manufacturer.Verification of the accuracy of the vehicle dynamics model.After the vehicle dynamics model is accurate and reliable,added the determined typical driving conditions of this type of car to the AVL Cruise M software Cycle Run calculation task,model calculates the exhaust temperature of the rear of the car turbine and the inlet of the DOC in each typical driving condition.According to the calculation results,the startup of active regeneration of DPF in each typical driving condition is determined,provide reliable and accurate temperature information for the development of DPF active regeneration control strategies.