Modeling fuel consumption in a heavy-duty diesel truck under real-world driving conditions including cold-start operation

Heavy-duty vehicles contribute significantly to global greenhouse gas emissions and are now facing challenges in meeting emission regulatory standards, with particular reference to cold-start operations. Advanced powertrains that make use of hybridization and waste heat recovery with phase-changing materials offer potential pathways to mitigate fuel consumption and emissions under real-world driving conditions. Still, they need to be accurately sized and managed to overcome the disadvantages of increased mass and complexity. This investigation paves the way for developing such advanced power systems by implementing a scalable map-based model for heavy-duty diesel engines. The model is validated using an open access data set related to a heavy-duty vehicle equipped with a 6-7L diesel engine performing 28 different trips on the same route with the same driver. The trips are performed with three different values of payload and contain both cold-start and hot-start operating conditions. The validation is based on a quasi-static modeling of the vehicle powertrain. The proposed model can predict fuel consumption and CO2 emissions in all trips with an average relative error of 2.4%. The results of the investigation also include the analysis of pollutant emissions during cold-start operation and the identification of a shifting strategy to be applied to regulatory driving cycles.