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

Author

Listed:
  • Chunling Wu

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
    China Automotive Technology and Research Center Co., Ltd., Tianjin 300300, 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
    Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China)

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.

Suggested Citation

  • Chunling Wu & Yiqiang Pei & Chuntao Liu & Xiaoxin Bai & Xiaojun Jing & Fan Zhang & Jing Qin, 2023. "Insights into the Fusion Correction Algorithm for On-Board NOx Sensor Measurement Results from Heavy-Duty Diesel Vehicles," Energies, MDPI, vol. 16(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6082-:d:1221238
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    References listed on IDEAS

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    1. Zhang, Shaojun & Wu, Ye & Hu, Jingnan & Huang, Ruikun & Zhou, Yu & Bao, Xiaofeng & Fu, Lixin & Hao, Jiming, 2014. "Can Euro V heavy-duty diesel engines, diesel hybrid and alternative fuel technologies mitigate NOX emissions? New evidence from on-road tests of buses in China," Applied Energy, Elsevier, vol. 132(C), pages 118-126.
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