Remote NOx emission monitoring for heavy duty diesel vehicles using categorical boosting

Zhihong Wang; Nenghui Yu; Jie Hu; Yuanjun Zhang; Longsheng Zhang

doi:10.1117/12.3031311

19 July 2024 Remote NOx emission monitoring for heavy duty diesel vehicles using categorical boosting

Zhihong Wang, Nenghui Yu, Jie Hu, Yuanjun Zhang, Longsheng Zhang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131814M (2024) https://doi.org/10.1117/12.3031311
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

The existing annual inspection and emission test cycle of motor vehicles have not effectively curbed diesel vehicle emissions, as real-world emissions can far exceed regulatory limits due to issues such as aging of aftertreatment systems and manipulation of sensor data. The China VI emission standard for heavy-duty diesel vehicles requires the installation of a remote on-board unit to upload real-time driving data to an online monitoring platform for emission regulation. The development of corresponding data analysis and application models is urgently needed. In this paper, the portable emission measurement system was employed to conduct real drive emissions tests on 3 heavy-duty diesel vehicles to obtain test data. The maximal relevance and minimal redundancy algorithm was applied to extract input features, and then the NOx transient emission prediction model was established using categorical boosting. The root mean square error of the prediction model on the test set is 1.034, with an average absolute error of 0.3907, an average absolute percentage error of 0.1443, and a coefficient of determination of 0.9047. Shapley additive explanation was applied to explain the feature importance of the model. This article offers useful insights for the online supervision of NOx emissions from heavy-duty diesel vehicles.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhihong Wang, Nenghui Yu, Jie Hu, Yuanjun Zhang, and Longsheng Zhang "Remote NOx emission monitoring for heavy duty diesel vehicles using categorical boosting", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131814M (19 July 2024); https://doi.org/10.1117/12.3031311

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KEYWORDS

NOx

Data modeling

Humidity

Performance modeling

Systems modeling

Education and training

Roads

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