Project Summary
This project aims to develop and validate a low-cost, automated, data-driven tool for identifying HGVs that generate excessive carbon emissions due to vehicle deficiencies (i.e. tyre wear) and inefficient driver behaviour.
Project Achievements
- Developed a numerical model of an HGV incorporating an automatic transmission module to predict an HGV’s fuel consumption. The model gives less than 2% prediction error.
- Developed a parameter estimator to estimate an HGVs’ mass and rolling resistance coefficient (RRC). Established two mass estimation methods: constant-speed method which applies to HGVs mainly operating on motorways, and gives <6% estimation error; and acceleration method which applies to HGVs mainly operating in cities, and gives <9% estimation error.
- Carried out coast-down tests of an HGV at HORIBA-MIRA proving ground to measure and quantify the influence of tyre under-inflation and axle misalignment on RRC. Developed a multi-level, logic-based algorithmic framework using test results for diagnosing HGV deficiencies including tyre under-inflation and axle misalignment.
Next Steps
- Further development of the proposed digital-twin-based method, including constructing thermal dynamics models for estimating tyre pressure, using additional signals to diagnose misalignment of a single drive axle, and adopting iterative methods to improve mass estimation accuracy.
- Initiate exploitation by providing free trials of the tool to selected HGV operators.
