Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model
- Title
- Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model
- Author
- 박성욱
- Keywords
- Road vehicle emission model; Artificial neural network; Vehicle dynamics; Real driving emission; Instantaneous emission
- Issue Date
- 2021-09
- Publisher
- ELSEVIER
- Citation
- SCIENCE OF THE TOTAL ENVIRONMENT, v. 786, article no. 147359, Page. 1-12
- Abstract
- 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.
- URI
- https://www.sciencedirect.com/science/article/pii/S004896972102430X?via%3Dihubhttps://repository.hanyang.ac.kr/handle/20.500.11754/177450
- ISSN
- 0048-9697;1879-1026
- DOI
- 10.1016/j.scitotenv.2021.147359
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > MECHANICAL ENGINEERING(기계공학부) > Articles
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