LAPSE:2023.31288
Published Article
LAPSE:2023.31288
Liquified Petroleum Gas-Fuelled Vehicle CO2 Emission Modelling Based on Portable Emission Measurement System, On-Board Diagnostics Data, and Gradient-Boosting Machine Learning
April 18, 2023
One method to reduce CO2 emissions from vehicle exhaust is the use of liquified petroleum gas (LPG) fuel. The global use of this fuel is high in European countries such as Poland, Romania, and Italy. There are a small number of computational models for the purpose of estimating the emissions of LPG vehicles. This work is one of the first to present a methodology for developing microscale CO2 emission models for LPG vehicles. The developed model is based on data from road tests using the portable emission measurement system (PEMS) and on-board diagnostic (OBDII) interface. This model was created from a previous exploratory data analysis while using gradient-boosting machine learning methods. Vehicle velocity and engine RPM were chosen as the explanatory variables for CO2 prediction. The validation of the model indicates its good precision, while its use is possible for the analysis of continuous CO2 emissions and the creation of emission maps for environmental analyses in urban areas. The validation coefficients for the selected gradient-boosting method of modelling CO2 emissions for an LPG vehicle are the R2 test of 0.61 and the MSE test of 0.77.
Keywords
Artificial Intelligence, Carbon Dioxide, emission modelling, LPG, Machine Learning, portable emission measurement system, vehicle emission
Suggested Citation
Mądziel M. Liquified Petroleum Gas-Fuelled Vehicle CO2 Emission Modelling Based on Portable Emission Measurement System, On-Board Diagnostics Data, and Gradient-Boosting Machine Learning. (2023). LAPSE:2023.31288
Author Affiliations
Mądziel M: Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland [ORCID]
Journal Name
Energies
Volume
16
Issue
6
First Page
2754
Year
2023
Publication Date
2023-03-15
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en16062754, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.31288
This Record
External Link

doi:10.3390/en16062754
Publisher Version
Download
Files
[Download 1v1.pdf] (3.7 MB)
Apr 18, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
142
Version History
[v1] (Original Submission)
Apr 18, 2023
 
Verified by curator on
Apr 18, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.31288
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version