A novel heavy-duty truck driving cycle construction framework based on big data

Depicting the driving characteristics of heavy-duty trucks (HDTs) under segmented usage scenarios is essential for their optimization design. This work presents a novel HDT driving cycle construction framework based on big data under segmented usage scenarios. To validate it, sixty million driving data of three thousand HDTs were collected, based on which six segmented cycles of two typical classes of HDTs (i.e., dump trucks and truck-tractors) are constructed. Then, systematic analyses between six segmented cycles are conducted, and the constructed cycles are compared to the legislated driving cycles in China (CHTC). The results indicate that the constructed driving cycles can well represent real-world driving data, and the cycle-specific fuel consumption from simulations accord with the empirics. It is found that segmented usage scenarios significantly affect the driving behaviors of HDTs. Furthermore, the constructed segmented driving cycles are superior over CHTC in the prediction accuracy of driving characteristics and fuel consumption.