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Quantifying Emissions in Vehicles Equipped with Energy-Saving Start–Stop Technology: THC and NOx Modeling Insights

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  • Maksymilian Mądziel

    (Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland)

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’s 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 R 2 scores of approximately 0.9 for engine emissions. Additionally, recommendations for effective modeling of such emissions from vehicles are presented in the paper.

Suggested Citation

  • Maksymilian Mądziel, 2024. "Quantifying Emissions in Vehicles Equipped with Energy-Saving Start–Stop Technology: THC and NOx Modeling Insights," Energies, MDPI, vol. 17(12), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2815-:d:1411014
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    References listed on IDEAS

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    1. de Paulo, Alex Fabianne & Nunes, Breno & Porto, Geciane, 2020. "Emerging green technologies for vehicle propulsion systems," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
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    3. Yu, Xiao & Sandhu, Navjot S. & Yang, Zhenyi & Zheng, Ming, 2020. "Suitability of energy sources for automotive application – A review," Applied Energy, Elsevier, vol. 271(C).
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