Open AccessResearch on Actual Road Emission Prediction Model of Heavy-Duty Diesel Vehicles Based on OBD Remote Method and Artificial Neural Network
Author(s) -
JiGuang Wang,
Li Wang,
Zhe Ji,
Songbo Qi,
Zhengchao Xie,
Zhiwen Yang,
Xiaowen Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012174
Subject(s) - diesel fuel , principal component analysis , automotive engineering , nox , heavy duty , diesel engine , artificial neural network , environmental science , transient (computer programming) , engineering , computer science , artificial intelligence , chemistry , organic chemistry , combustion , operating system
In order to establish a prediction model of PM and NOx emission factors for heavy-duty diesel vehicles under actual road conditions based on OBD remote monitoring and big data, this paper carried out actual road tests on two China V heavy-duty diesel vehicles to obtain transient OBD and emission data by a Portable Emission Measurement System (PEMS) and self-developed On-board Remote Emission Measurement System (OREMS) . According to the degree of influence of different parameters in the engine OBD on PM and NOx emissions, the principal component analysis method is used to extract the principal component parameters used to predict the model input, and the construction of a "Heavy-duty Diesel Vehicle Predictive Model based on Remote Monitoring Data and Neural Network Technology". Finally, the predictive model is trained and verified by PEMS test data. The prediction model provides new means and methods for the future development of large-scale heavy-duty diesel vehicle NOx and PM emission predictions under actual road operation.