Modeling, simulation, and validation of E-scooter braking dynamics using multibody methods

Vella, Angelo Domenico; Digo, Elisa; Vigliani, Alessandro · 2026 · Crossref

DOI: 10.1007/s11044-025-10102-z

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Summary

This study addresses the insufficient understanding of the complex dynamic interaction between electric kick scooters (e-scooters) and their riders, specifically during braking maneuvers. While e-scooters are increasingly vital for urban mobility, their stand-up riding posture and small tires present unique safety and stability challenges distinct from other two-wheeled vehicles. Previous research has largely focused on vehicle dynamics or used simplified rider models, neglecting the significant influence of human body motion on coupled system dynamics. To bridge this gap, the authors developed and validated a high-fidelity multibody model that integrates a commercial e-scooter with a semi-active anatomical dummy representing the rider. The methodology employed a detailed in-plane multibody system developed in Matlab/Simscape. The vehicle model included a front wheel motor and rear disc braking system, with tire-ground interactions simulated using the MF-Tyre module. The rider was modeled as a 14-segment, 37-degree-of-freedom system with semi-active joint control logic to simulate human response to inertial forces. The model was tuned and validated against experimental data collected from a healthy male rider performing light, medium, and heavy braking maneuvers on a Xiaomi Pro II e-scooter. Data acquisition involved inductive tachometers for wheel velocity, an AHRS for vehicle acceleration, and magneto-inertial measurement units (MIMUs) for rider kinematics. An optimization algorithm combining Genetic Algorithms and Pattern Search was used to minimize the discrepancy between numerical simulations and experimental signals by adjusting rider joint control parameters. The results demonstrated strong agreement between the simulated and experimental data across all braking intensities. During heavy braking, the model accurately captured the rear wheel lock-up and the characteristic oscillations in longitudinal acceleration caused by rider motion. The simulation correctly reproduced the rider’s kinematic response, including the initial backward rotation of the trunk and subsequent forward adjustment to maintain equilibrium, as well as the corresponding flexion and extension of the hip and ankle joints. Pearson correlation coefficients indicated moderate to very strong correlations (average R = 0.75) for human body segments, with the model performing best for the trunk during light braking and for the legs during heavy braking. Vehicle acceleration correlations were lower, attributed to signal-to-noise issues at lower braking intensities. The significance of this work lies in providing a robust, validated computational tool for analyzing e-scooter dynamics that accounts for rider-vehicle interaction. By demonstrating that rider motion significantly influences vehicle behavior, the study highlights the necessity of including human factors in safety and control strategy design. The validated model offers valuable insights for enhancing the safety, comfort, and maneuverability of e-scooters, supporting future developments in vehicle design and risk mitigation for urban micro-mobility.

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• micromobility e scooter