Method for rapidly predicting automobile PN emission based on oil product aromatic hydrocarbon composition

Abstract

The invention provides a method for rapidly predicting automobile PN emission based on oil product aromatic hydrocarbon composition, and the method comprises the following steps: S1, blending gasoline samples, each gasoline sample having different distillation ranges, aromatic hydrocarbon content and benzene with different carbon numbers, and the distillation ranges comprise 50% evaporation temperature and 90% evaporation temperature; carrying out emission test on each gasoline sample after real-vehicle I-type normal-temperature cold start, and measuring the emission amount of discharged PN to obtain emission data of PN; and S2, matching each gasoline sample in the step A1 with the obtained corresponding PN emission data to form a prediction data set. The method has the beneficial effects that regression fitting is performed on the PN emission value of the to-be-tested sample by adopting the partial least square method, the model of the partial least square method is established by using the test set participating in fitting, then the verification set is substituted into the model for prediction, the decision coefficient is calculated according to the prediction result of the verification set, and the accuracy of the prediction result is improved. And combining the test set with the verification set to establish a mathematical model for finally predicting the PN emission value.

Description

technical field [0001] The invention belongs to the field of automobile emission, and in particular relates to a method for rapidly predicting the PN emission of automobiles based on the composition of oil aromatics. Background technique [0002] With the rapid increase in the number of automobiles, strengthening automobile emission control is an important issue in the field of urban air pollution prevention and control. In the "vehicle-oil-road" automobile pollution prevention and control system, oil quality has an important impact on engine performance, especially emission performance. The increasingly stringent vehicle emission regulations have promoted the continuous progress of internal combustion engine emission control technology, such as the rapid development of diesel engine ultra-high fuel injection, variable intake and exhaust system and after-treatment device technology, which further requires the accelerated development of clean fuel for vehicles to meet the nee...

AI Technical Summary

Problems solved by technology

[0003] Toyota Aikawa proposed the PM index model to perform detailed monomer hydrocarbon analysis on representative oil products, combined with the boiling point, mass fraction and vapor pressure of each hydrocarbon compound, which has a good prediction effect, but the calculation itself is cumbersome, and it is difficult for heavy aromatics. The component distinction is not obvious, the prediction of components such as dicyclopentadiene fails, and the adaptability and accuracy are poor

[0004] To sum up, due to the complex composition of heavy aromatics, the sensitivity and accuracy of prediction of one or several components of heavy aromatics are low, so it is not suitable for rapid monitoring of PN emissions from vehicles through oil product indicators and aromatics composition. predictive analysis

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