In this study, a comprehensive analysis is provided regarding the relationship between a virtual vehicle model of an electric motorcycle and real-world consumption data. Special emphasis is placed on the possibilities for reducing energy consumption, and the impact of various interventions on the amount of energy utilized is presented. Although motorcycles do not play a significant role in transportation within the Central European region, the demonstrated methods for reducing consumption can also be applied and utilized for other vehicles, such as passenger cars. During the investigation, a virtual vehicle model and an onboard measurement system installed on the motorcycle were employed. The model was successfully validated, thereby serving as a reliable tool for estimating and forecasting energy consumption. Throughout the research, it was highlighted that analyzing factors influencing energy consumption is essential for improving energy efficiency. An interesting finding revealed that human factors have the greatest impact on actual energy consumption, potentially opening new research directions in energy-efficient transportation. It is important to note that although this study focuses on a specific vehicle type, the methodology and insights can be applied more broadly, particularly to other vehicle categories. The presented results further confirm the significance of examining driver behavior and driving styles, as these factors play a pivotal role in enhancing energy efficiency. Beyond the technical contributions, the findings also underline an important connection to cognitive mobility. As smart mobility systems increasingly require users to understand, evaluate, and adopt novel transportation technologies, the ability of drivers and riders to cognitively adapt becomes central.