Development of realistic driving cycles via incremental Markov chains for sport motorcycles

Driving cycles are essential for vehicle certification, fuel consumption and emissions estimation. However, current approval cycles do not accurately capture the dynamics of sport motorcycles, leading to misrepresentation of riding conditions. This study addresses this gap by developing the Sport Motorcycle Driving Cycle (SMDC) using real-world data collected from a high-performance motorcycle. Data analysis revealed that key indicators, as the characteristic acceleration (a˜) and aerodynamic speed (Vaer2), differ significantly from type-approval cycles and strongly correlate with fuel consumption and engine power. The SMDC was synthesized via a novel method combining a 3D Markov Chain (speed, acceleration, and road slope) with incremental random walks. This approach achieved an energy consumption error of -0.31 % while reducing the cycle length by 90.7 % compared to the reference dataset. These results underscore the discrepancies between approval cycles and real sport riding, advocating for revised homologation protocols and offering a new tool for design and evaluation.