Article
Version 1
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Quantifying Emissions in Vehicles Equipped with Energy-Saving Start-Stop Technology: THC and NOx Modeling Insights
Version 1
: Received: 30 April 2024 / Approved: 30 April 2024 / Online: 1 May 2024 (07:32:46 CEST)
A peer-reviewed article of this Preprint also exists.
Mądziel, M. Quantifying Emissions in Vehicles Equipped with Energy-Saving Start–Stop Technology: THC and NOx Modeling Insights. Energies 2024, 17, 2815. Mądziel, M. Quantifying Emissions in Vehicles Equipped with Energy-Saving Start–Stop Technology: THC and NOx Modeling Insights. Energies 2024, 17, 2815.
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.
Keywords
vehicles; start-stop; emission; PEMS; pollution; THC; NOx; modeling; artificial intelligence
Subject
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.
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