Insights into the Fusion Correction Algorithm for On-Board NOx Sensor Measurement Results from Heavy-Duty Diesel Vehicles

Chunling Wu, Yiqiang Pei (), Chuntao Liu, Xiaoxin Bai, Xiaojun Jing, Fan Zhang and Jing Qin
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Chunling Wu: State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
Yiqiang Pei: State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
Chuntao Liu: State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
Xiaoxin Bai: China Automotive Technology and Research Center Co., Ltd., Tianjin 300300, China
Xiaojun Jing: China Automotive Technology and Research Center Co., Ltd., Tianjin 300300, China
Fan Zhang: State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
Jing Qin: State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China

Energies, 2023, vol. 16, issue 16, 1-19

Abstract: Over the last decade, Nitrogen Oxide (NOx) emissions have garnered significantly greater attention due to the worldwide emphasis on sustainable development strategies. In response to the issues of dynamic measurement delay and low measurement accuracy in the NOx sensors of heavy-duty diesel vehicles, a novel Multilayer Perceptron (MLP)–Random Forest Regression (RFR) fusion algorithm was proposed and explored in this research. The algorithm could help perform post-correction processing on the measurement results of diesel vehicle NOx sensors, thereby improving the reliability of the measurement results. The results show that the measurement errors of the On-board Nitrogen oxide Sensors (OBNS) were reduced significantly after the MLP-RFR fusion algorithm was corrected. Within the concentration range of 0–90 ppm, the absolute measurement error of the sensor was reduced to ±4 ppm, representing a decrease of 73.3%. Within the 91–1000 ppm concentration range, the relative measurement error was optimised from 35% to 17%, providing a reliable solution to improve the accuracy of the OBNS. The findings of this research make a substantial contribution towards enhancing the efficacy of the remote monitoring of emissions from heavy-duty diesel vehicles.

Keywords: heavy-duty diesel vehicles; on-board nitrogen oxide sensors (OBNS); fusion correction algorithm; multilayer perceptron (MLP)–random forest regression (RFR); machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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