Design of electric motorcycles using right-sizing criteria and framework definition for analysis and synthesis of driving cycles

This PhD thesis focuses on the sizing of the powertrain for high-performance electric motorcycles, addressing both the technical challenges and the optimization opportunities available in this emerging field. The research is structured into eight chapters, each contributing to a comprehensive understanding of electric motorcycle powertrain design. Chapter 1 provides an introduction to electric vehicles, covering the electrification of road vehicles, the fundamentals of electric vehicle technology, their limits, and an analysis of the electric motorcycle market and its adoption trends worldwide. In Chapter 2, the state-of-the-art is reviewed, detailing driving cycles, benchmarking of market sold electric motorcycles, energy consumption flow, vehicle modeling, energy sources for electric vehicles, utilization of the electric machine and driveline for electric vehicle traction. Chapter 3 summarizes the key original contributions of this work, laying the foundation for novel methodologies and design strategies. Chapter 4 describes vehicle dynamics, both longitudinal and lateral, with a particular focus on energy consumption aspects. Chapter 5 describes the synthesis of driving cycles and the development of the Sport Motorcycle Driving Cycle (SMDC), tailored to capture the performance requirements of high-performance electric motorcycles, including the altitude profile. Chapter 6 analyzes the powertrain losses, providing a detailed evaluation of where and how energy is dissipated and its influence on consumption. Finally, Chapter 7 explores vehicle optimization, specifically examining the influence of brake blending and the joint optimization of battery, motor, and gear ratio for a two-wheel-driven superbike, culminating in recommendations for enhanced performance and efficiency. Chapter 8 concludes the thesis by summarizing the findings, discussing their implications, limits, and suggesting hints for future research.