Version 1
: Received: 6 September 2024 / Approved: 6 September 2024 / Online: 9 September 2024 (09:17:40 CEST)
How to cite:
Mądziel, M. Reverse Engineering: Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies. Preprints2024, 2024090588. https://doi.org/10.20944/preprints202409.0588.v1
Mądziel, M. Reverse Engineering: Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies. Preprints 2024, 2024090588. https://doi.org/10.20944/preprints202409.0588.v1
Mądziel, M. Reverse Engineering: Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies. Preprints2024, 2024090588. https://doi.org/10.20944/preprints202409.0588.v1
APA Style
Mądziel, M. (2024). Reverse Engineering: Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies. Preprints. https://doi.org/10.20944/preprints202409.0588.v1
Chicago/Turabian Style
Mądziel, M. 2024 "Reverse Engineering: Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies" Preprints. https://doi.org/10.20944/preprints202409.0588.v1
Abstract
In the face of increasing environmental protection requirements, modeling emissions from older vehicles presents a significant challenge. This paper introduces an innovative methodology for developing emission models for older vehicles by employing advanced AI and machine learning techniques. The study utilized algorithms, including gradient boosting, to analyze data from road tests and the OBDII diagnostic interface, allowing the creation of precise models for CO₂, CO, THC and NOx emissions. A key achievement is the implementation of input data clustering, which significantly enhances the accuracy of emission forecasts. Model validation demonstrated high precision, with notable R² coefficients and mean squared errors. The developed models serve as a crucial tool for analyzing and managing emissions in the context of an aging vehicle fleet, providing new opportunities to shape transportation policy and strategies for pollution reduction.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
