A Novel Parameter Estimation Scheme for Vehicle Suspension Systems Based on Response and Test Track Prioritization

Kanchwala, Husain; Ahiska, Kenan · 2023 · Crossref

DOI: 10.3390/app131810312

archive: archived pipeline: cataloged verified

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Summary

This paper presents a novel system identification methodology for estimating vehicle suspension parameters using time-domain data collected from driving on various road profiles, eliminating the need for specialized test rigs or controlled excitation inputs. The research addresses the challenge of accurately identifying twelve critical vehicle ride parameters—including suspension stiffness and damping, tire characteristics, mass properties, and inertias—which are essential for evaluating ride comfort but are often difficult to measure directly. The authors motivate this approach by noting that traditional methods frequently rely on expensive hardware-in-the-loop setups or frequency-domain analyses with specific excitation signals, whereas this method leverages the natural dependence of parameter identifiability on vehicle responses and test track characteristics. The proposed method utilizes a seven-degree-of-freedom full-car ride model developed in MATLAB to map the relationship between vehicle responses and the strength of different test tracks in exciting those responses. Three deterministic test tracks—chassis-twist, herringbone, and washboard—were selected for their varying bump amplitudes and wavelengths. To prioritize these tracks, the authors performed a preliminary analysis to weigh responses and tracks based on their capability to estimate specific parameters. The parameter estimation itself is conducted using an optimization framework based on the JADE (JADE: adaptive differential evolution with an external archive) algorithm, a heuristic global optimization technique chosen for its fast convergence and efficiency in handling complex parameter dependencies. The study validates the methodology through a multi-stage process. First, the approach is verified using a high-fidelity multi-body dynamics model developed in ADAMS, which incorporates flexible chassis components and detailed suspension geometry. This simulation confirms the algorithm’s ability to estimate parameters accurately. Subsequently, experimental validation is performed on an all-terrain vehicle (ATV) instrumented with eight MEMS accelerometers and an inertial measurement unit (IMU) to capture axle accelerations, suspension-to-body attachment point accelerations, and body orientation rates. The vehicle was driven on the selected test tracks, and the collected time-series data were processed using the JADE algorithm. The results demonstrate that the proposed scheme can identify the twelve vehicle suspension parameters with high accuracy and fast convergence. The prioritization of test tracks based on their ability to reduce the spread of parameter estimates in the optimization population proved effective in enhancing identification performance. The authors conclude that this method offers a practical, cost-effective alternative to traditional system identification techniques, as it relies on easily installable sensors and standard driving maneuvers rather than specialized equipment. This contributes to the field by providing a systematic framework for field-based vehicle parameter estimation that accounts for the varying influence of different road profiles on parameter identifiability.

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• exposure measurement