Predicting of Transport Carbon Dioxide Emission of Light-Duty Gasoline Vehicles Based on Driving Behavior with Machine Learning Algorithm China Automotive Engineering Research Institute Co Limited

Format:

Book

Conference/Event

Author/Creator:

Yu, Hao, author.

Contributor:

Chen, Hao

Hu, Wei

Ma, Yi

Tan, Jianxun

Wang, Jing

Yu, Wenbin

Zhang, Honghao

Conference Name:

Automotive Technical Papers (2026-01-01 : Warrendale, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Driving behavior is a significant factor influencing vehicle emissions, and it must be carefully considered when modeling emissions for real road transportation vehicles. This study aims to contribute to this field by improving the intelligence and accuracy of distinguishing driving behavior volatility through the use of clustering algorithm. The research begins by processing raw emissions data collected from light-duty gasoline vehicle during real-driving emissions (RDE) test, which are used as input features for the clustering algorithm. Subsequently, a driving behavior classification method based on the gaussian mixture model (GMM) clustering algorithm is proposed. The results show that aggressive driving has a significantly higher CO2 emission rate compared to normal and calm driving. Specifically, the average CO2 emission rate for aggressive driving is 5.61 g/s, which is substantially higher than that of calm driving (2.40 g/s) and normal driving (2.91 g/s). Following this, the study employs Pearson correlation coefficients and an intelligent machine learning modeling approach, incorporating driving behaviorrelated parameters as part of the input features for the virtual CO2 emission prediction model for light-duty gasoline vehicles. This further highlights the effectiveness of using a clustering algorithm for driving behavior classification, and it demonstrates the significant impact of driving behaviorrelated parameters on CO2 emission rates, providing an accurate emission model for virtual calibration of vehicles

Notes:

Vendor supplied data

Publisher Number:

2026-01-5012

Access Restriction:

Restricted for use by site license