On the Road Profile Estimation from Vehicle Dynamics Measurements

Vella, Angelo Domenico; Tota, Antonio; Vigliani, Alessandro · 2021 · Crossref

DOI: 10.4271/2021-01-1115

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Summary

This paper addresses the challenge of estimating road profiles from vehicle dynamics measurements, a task critical for ride comfort assessment, tire load estimation, and suspension control. Direct measurement of road irregularities using professional instrumentation is often prohibitively expensive and time-consuming. Consequently, the authors propose an indirect estimation method using a linear Kalman filter algorithm that relies on accessible, traditional onboard sensors. The approach utilizes pitch/bounce half-car models for the prediction phase and measures vertical accelerations and angular speeds for the correction phase. The study employs a two-passenger electric quadricycle from the European STEVE project as the test vehicle. Due to a lack of manufacturer data for suspension stiffness and damping, the authors performed preliminary measurements on the front coil springs to estimate suspension stiffness, calculating a theoretical stiffness of 27,165 N/m. Experimental tests involved driving the vehicle over a vulcanized rubber road bump at constant longitudinal speeds of 10, 20, and 30 km/h. Data acquisition utilized an LMS Siemens SCADAS XS system, capturing signals from a Kistler Correvit S-Motion sensor (for speed, pitch angle, and rate) and three PCB tri-axial accelerometers placed on the passenger seat, the upper attachment of the front-right shock absorber, and the front-right lower control arm. Signals were resampled at 1000 Hz and filtered to remove drift and high-frequency noise. To validate the estimator, the authors first calibrated a 4-degree-of-freedom pitch/bounce half-car numerical model against the experimental data. This model merges the left and right sides of the vehicle, neglecting roll motion, and includes sprung and unsprung masses connected by springs and dampers. The calibrated model was then integrated into the linear Kalman filter to estimate the road profile. The experimental results demonstrated that the tests were repeatable across different bumps and speeds. While vertical accelerations measured by the seat cushion and the door-mounted sensor were similar, the door signal exhibited high-frequency oscillations due to material properties and damping foam. The constant speed requirement was successfully maintained, ensuring that pitch motion was excited solely by the road input rather than longitudinal acceleration. The significance of this work lies in providing a robust and efficient algorithm for indirect road profile evaluation using standard vehicle sensors. By successfully applying the Kalman filter-based estimator to experimental data, the authors demonstrate that accurate road profile estimation is feasible without expensive direct measurement tools. This method offers a practical solution for applications requiring real-time knowledge of road conditions, such as active suspension control and durability analysis, thereby reducing costs and complexity in vehicle dynamics testing and simulation.

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