0D Modeling of Real-Driving NOx Emissions for a Diesel Passenger Vehicle

2018-01-1761

09/10/2018

Features
Event
International Powertrains, Fuels & Lubricants Meeting
Authors Abstract
Content
NOx emissions from diesel passenger vehicles affect the atmospheric environment. It is difficult to evaluate the NOx emissions influenced by environmental conditions such as humidity and temperature, traffic conditions, driving patterns, etc. In the authors’ previous study, real-driving experiments were performed on city and highway routes using a diesel passenger car with only an exhaust gas recirculation system. A statistical prediction model of NOx emissions was considered for simple estimations in the real world using instantaneous vehicle data measured by the portable emissions measurement system and global positioning system. The prediction model consisted of explanatory variables, such as velocity, acceleration, road gradient, and position of transmission gear. Using the explanatory variables, NOx emissions on the city and highway routes was well predicted using a diesel vehicle without NOx reduction devices. However, the prediction model had some limitations owing to the effects of NOx reduction devices. In this study, among various NOx reduction systems, a diesel vehicle with NOx storage catalyst (NSC) was chosen to predict the NOx emissions under a catalytic system. To improve the accuracy of the NOx emissions under the NSC, a catalyst model was added to the prediction model and used to predict the catalyst properties. By adding the catalyst model, the accuracy of NOx emissions of the prediction model was improved compared to the previous prediction model with individual explanatory variables. The NOx emissions were well predicted compared to the measured data.
Meta TagsDetails
DOI
https://doi.org/10.4271/2018-01-1761
Pages
11
Citation
Kim, S., Kuboyama, T., Moriyoshi, Y., and Suzuki, H., "0D Modeling of Real-Driving NOx Emissions for a Diesel Passenger Vehicle," SAE Technical Paper 2018-01-1761, 2018, https://doi.org/10.4271/2018-01-1761.
Additional Details
Publisher
Published
Sep 10, 2018
Product Code
2018-01-1761
Content Type
Technical Paper
Language
English