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In the city of Cuenca – Ecuador (2550 m.a.s.l), a significant introduction of HEVs in the automotive fleet is observed. These vehicles are adopted due to measures aimed at reducing fuel consumption and harmful emissions. Due to high-power cold start phenomena and deficiencies in catalytic converters, CO emissions show high increases compared to conventional vehicles at sea level. This necessitates the present analysis, which is based on data acquisition on “RDE” routes using data logger equipment to measure operational, positioning, and emission parameters. This analysis leverages machine learning skills linked to ANNs and Random Forest, aiming to estimate the pollutants of hybrids and demonstrate the influence of acceleration on each gas and calculated operational traction parameter (ICE-EM Powers, Aerodynamic Traction Force, SOC, etc.) in high-altitude cities. The results indicate a strong relationship of the longitudinal acceleration vector on CO2 and NOx gases, twice as high as CO and HC, corroborating related studies mentioning that emissions reduction principles are most closely related to acceleration in the present investigation.

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Influence of the Acceleration Vector on Measured and Estimated Pollutant Emissions of Hybrid Vehicles, Focusing on Traction and Power Operating Parameters

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Jackson Steeven Vidal Suarez,
  • Walter Josué Semiglia Pineda,
  • Néstor Diego Rivera Campoverde

摘要

In the city of Cuenca – Ecuador (2550 m.a.s.l), a significant introduction of HEVs in the automotive fleet is observed. These vehicles are adopted due to measures aimed at reducing fuel consumption and harmful emissions. Due to high-power cold start phenomena and deficiencies in catalytic converters, CO emissions show high increases compared to conventional vehicles at sea level. This necessitates the present analysis, which is based on data acquisition on “RDE” routes using data logger equipment to measure operational, positioning, and emission parameters. This analysis leverages machine learning skills linked to ANNs and Random Forest, aiming to estimate the pollutants of hybrids and demonstrate the influence of acceleration on each gas and calculated operational traction parameter (ICE-EM Powers, Aerodynamic Traction Force, SOC, etc.) in high-altitude cities. The results indicate a strong relationship of the longitudinal acceleration vector on CO2 and NOx gases, twice as high as CO and HC, corroborating related studies mentioning that emissions reduction principles are most closely related to acceleration in the present investigation.