Sensor Review, Volume 35, Issue 2, March 2015.
Purpose Wheel force transducers (WFT) 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 WFT is mounted on the wheel in driven, the loads on the wheel and the outputs of WFT are usually nonlinearly related. Thus, a real-time filter is needed to measure the true loads on the wheel. Design/methodology/approach 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. Findings The simulation results show that the accuracy and the rapidity of QKF outperform 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, 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 validate the proposed method fitting reality. Originality/value A newly improved algorithm with QKF for WFT has been proposed and tested experimentally in this paper. 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 quadrature Kalman filter are established. The experiment results show that QKF has advanced performance than the traditional UKF. Plus, 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.
Purpose Wheel force transducers (WFT) 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 WFT is mounted on the wheel in driven, the loads on the wheel and the outputs of WFT are usually nonlinearly related. Thus, a real-time filter is needed to measure the true loads on the wheel. Design/methodology/approach 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. Findings The simulation results show that the accuracy and the rapidity of QKF outperform 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, 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 validate the proposed method fitting reality. Originality/value A newly improved algorithm with QKF for WFT has been proposed and tested experimentally in this paper. 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 quadrature Kalman filter are established. The experiment results show that QKF has advanced performance than the traditional UKF. Plus, 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.
