Synthesis of Representative Driving Cycle for Heavy Duty Vehicle Based on Markov Chain and Big Data Considering Weight Variation 2023-32-0177
Synthesized driving cycles which can reflect the real world driving scenarios are essential for electrification and hybridization of powertrains of heavy duty logistics vehicles (HDLV). Current synthetic methods always neglected weight variation which is crucial for logistic vehicle driving scenarios. This paper proposed a method based on multi-dimensional Markov chains and big data to generate typical driving cycles with consideration of vehicle weight and slope. The validation of the synthesized driving cycle was based on a statistical analysis and the adequacy of the representative to real world driving data was demonstrated.
