Sustainable Emission Control in Heavy-duty Diesel Trucks through Fuzzy-logic-based Multi-source Diagnostic Approach

Strategic Insights -

Fresh data on Sustainability Research has shed light on a new report that reveals the effectiveness of a fuzzy-logic-based multi-source diagnostic approach in reducing emissions from heavy-duty diesel trucks. According to researchers at Nanjing Forestry University, the approach integrates load deceleration testing, on-board diagnostics, and manual measurements to systematically select critical diagnostic parameters and develop an adaptive membership function to resolve ambiguities in emission thresholds. The proposed model demonstrates a diagnostic accuracy of 92.8% for 153 emission-exceeding vehicles, surpassing traditional machine learning approaches by over 20%.

Key Takeaways:

  • The fuzzy-logic-based multi-source diagnostic approach integrates load deceleration testing, on-board diagnostics, and manual measurements to select critical diagnostic parameters, including nitrogen oxides (NOx) and particulate matter (PM) emissions.
  • The approach develops an adaptive membership function to resolve ambiguities in emission thresholds, enabling the construction of a robust fault diagnosis framework.
  • The proposed model demonstrates a diagnostic accuracy of 92.8% for 153 emission-exceeding vehicles, surpassing traditional machine learning approaches by over 20%.
  • The approach minimizes unnecessary repairs and optimizes maintenance efficiency, significantly reducing resource waste and the lifecycle environmental footprints of diesel fleets.
  • The research aims to contribute to sustainable transportation through reductions in NOx and PM emissions, critical for improving air quality and advancing global climate objectives.
  • The proposed technical framework is scalable and can be effectively implemented in alignment with sustainable urban mobility policies.
  • The study highlights the importance of multi-source information fusion in addressing the challenge of blind repairs at maintenance stations.
  • The research was funded by the Jiangsu Provincial Transportation Science and Technology Project, Research on the Control Technology of Excessive Pollutant Emissions of Operating Diesel Trucks.

Statistics:

  • The proposed model demonstrated a 92.8% diagnostic accuracy for 153 emission-exceeding vehicles.
  • The approach surpassed traditional machine learning approaches by over 20% in diagnostic accuracy.
  • The study used 800 National V diesel truck maintenance records from a provincial automotive electronic health platform (2022 data).
  • The approach minimizes unnecessary repairs by 20% and optimizes maintenance efficiency by 25%.

Sources:

  • [1] Sustainable Emission Control In Heavy-duty Diesel Trucks: Fuzzy-logic-based Multi-source Diagnostic Approach, Sustainability, 2025; 17(8): 3605.
  • [2] NewsRx. Investigators at Nanjing Forestry University Detail Findings in Sustainability Research (Sustainable Emission Control In Heavy-duty Diesel Trucks: Fuzzy-logic-based Multi-source Diagnostic Approach). Ecology, Environment & Conservation. May 30, 2025; p 204.