Wheel force transducers (WFTs) have performance characteristics that make them attractive for applications in endurance evaluation of road vehicles, ride and handling optimization, tire development and vehicle dynamics. As a WFT is mounted on the the driven wheel, the loads on the wheel and the outputs of WFTs are usually nonlinearly related. Thus, a real-time filter is needed to measure the true loads on the wheel.
In this paper, a new nonlinear filtering algorithm utilizing quadrature Kalman filter (QKF) is proposed to track the actual loads in real time through establishing the specific observation equations with Singer models.
The simulation results show that the accuracy and the rapidity of QKF outperforms the capability of the unscented Kalman filter (UKF). Then, the dynamic tests on the MTS testing platform give the comparisons between the real-time QKF and the wavelet transform, where the former has superior dynamic accuracy. Finally, the practical tests of shifting and braking on a real vehicle confirm the effectiveness of QKF, which further validates the proposed method fitting reality.
In this paper, a newly improved algorithm with QKF for WFT has been proposed and tested experimentally. As the wheel loads are always time-varying and complex, introducing Gaussian noise in the outputs of the transducer, WFT-suitable Singer model and WFT measurement equation base on a QKF are established. The experiment results show that QKF has advanced performance than the traditional UKF. Also, the road wheel test bed produced by MTS has been exploited as the test platform to demonstrate the dynamic efficiency of the proposed real-time filter under various operating conditions for a wide range of loads. And, the practical tests with the real vehicle are accomplished to verify the value and effectiveness of the proposed method.
This work was financed by Grant-in-aid for scientific research from Natural Science Foundation of China (Grants 51305078) and Suzhou Science and Technology Project (Grant SYG201303).
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