Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model

This paper presents a road vehicle emission model that integrates an artificial neural network (ANN) model with a vehicle dynamics model to predict the instantaneous carbon dioxide (CO₂), nitrogen oxides (NOₓ) and total hydrocarbon (THC) emissions of diesel light-duty vehicles. Real-world measurement data were used to train a multi-layer feed-forward ANN model. The optimal combination of the various experimental variables was selected as the ANN input through a parametric study considering both practicality and accuracy. For CO₂ prediction, two variables (engine speed and engine torque) are enough to develop an accurate ANN model. In order to achieve satisfactory accuracy for CO and NOₓ prediction, more variables were used for ANN training. The trained ANN model was used to predict road vehicle emissions by integrating the vehicle dynamics model, which was used as a supplementary tool to produce ANN input data. The integrated model is practical because it requires relatively simple data for input such as vehicle specifications, velocity, and road gradient. In the accuracy validation, the proposed model showed satisfactory prediction accuracy for road vehicle emissions.

• Record URL:
  https://doi.org/10.1016/j.scitotenv.2021.147359
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S004896972102430X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00489697
• Supplemental Notes:
  • © 2021 Elsevier B.V. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Seo, Jigu
  • Yun, Boseoup
  • Park, Jisu
  • Park, Junhong
  • Shin, Myounghwan
  • Park, Sungwook
• Publication Date: 2021-9-10

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Serial:
  • Science of the Total Environment
  • Volume: 786
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0048-9697
  • Serial URL: http://www.sciencedirect.com/science/journal/00489697

Subject/Index Terms

• TRT Terms: Diesel engine exhaust gases; Diesel engines; Light vehicles; Machine learning; Motor vehicle dynamics; Neural networks; Predictive models; Real time data processing
• Subject Areas: Environment; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01774884
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 24 2021 4:40PM