Mass Data Measurement, Approximation and Influence on Vehicle Stability for Ultra-Light Human-Powered Vehicles

• Main Authors: Toni Wilhelm, Volker Dorsch, Frank Gauterin
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Applied Sciences
• Subjects: three-wheeled vehicle; inertia estimation; human-powered vehicle; mass properties; bicycle; inertia measurement
• Online Access: https://www.mdpi.com/2076-3417/11/12/5485

The mass properties of a vehicle play a decisive role in its dynamics and characteristics and are fundamental for vehicle dynamics models and controllers. These values are not yet known for the vehicle class of the ultra-light velomobiles and similar multi-track bicycle vehicles. In the future, however, such vehicles could play a role in reducing the CO₂ emissions generated by individual transportation. As a basis for vehicle dynamics modeling, accident reconstruction, and controller development for this vehicle class, this paper investigated ranges of mass properties and their influence on vehicle stability considering driver influence. In total, 13 vehicles (10 velomobiles and 3 trikes) were examined using different experimental setups. It was shown that most vehicles exhibited understeering behavior based on the center of gravity position and calculations of the static stability factor showed significantly lower rollover stability compared with conventional vehicles. The measured moments of inertia were used to develop and examine different approximation approaches for the yaw moment of inertia using conventional approaches from the passenger car sector and stepwise regression. This created the basis for parameter estimation from easily measurable vehicle parameters and provided the possibility to generate realistic parameter sets for vehicle dynamic models. Existing tests do not consider the influence of driver movements, such as pedaling movements or possible inclination of the upper body. This offers the potential for further investigations of the dynamic influences on the investigated variables.