Version 1
: Received: 30 April 2024 / Approved: 30 April 2024 / Online: 1 May 2024 (07:32:46 CEST)
How to cite:
Mądziel, M. Quantifying Emissions in Vehicles Equipped with Energy-Saving Start-Stop Technology: THC and NOx Modeling Insights. Preprints2024, 2024050024. https://doi.org/10.20944/preprints202405.0024.v1
Mądziel, M. Quantifying Emissions in Vehicles Equipped with Energy-Saving Start-Stop Technology: THC and NOx Modeling Insights. Preprints 2024, 2024050024. https://doi.org/10.20944/preprints202405.0024.v1
Mądziel, M. Quantifying Emissions in Vehicles Equipped with Energy-Saving Start-Stop Technology: THC and NOx Modeling Insights. Preprints2024, 2024050024. https://doi.org/10.20944/preprints202405.0024.v1
APA Style
Mądziel, M. (2024). Quantifying Emissions in Vehicles Equipped with Energy-Saving Start-Stop Technology: THC and NOx Modeling Insights. Preprints. https://doi.org/10.20944/preprints202405.0024.v1
Chicago/Turabian Style
Mądziel, M. 2024 "Quantifying Emissions in Vehicles Equipped with Energy-Saving Start-Stop Technology: THC and NOx Modeling Insights" Preprints. https://doi.org/10.20944/preprints202405.0024.v1
Abstract
Creating accurate emission models capable of capturing the variability and dynamics of modern propulsion systems is crucial for future mobility planning. This paper presents a methodology for creating THC and NOx emission models for vehicles equipped with start-stop technology. A key aspect of this endeavor is to find techniques that accurately replicate the engine stop stages when there are no emissions. To this end, several machine learning techniques were tested using the Python programming language. Random forest and gradient boosting methods demonstrated the best predictive capabilities for THC and NOx emissions, achieving R2 scores of approximately 0.9 for both cold and hot engine emissions. Additionally, recommendations for effective modeling of such emissions from vehicles are presented in the paper.
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.
