Adaptive joint estimates of vehicle mass and road grades for small acceleration driving scenarios

SUN Enxin; YIN Yuming; XIN Zhe; LI Shengbo; HE Jugang; KONG Zhouwei; LIU Xiupeng

doi:10.16511/j.cnki.qhdxxb.2021.21.030

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Journal of Tsinghua University(Science and Technology) ›› 2022, Vol. 62 ›› Issue (1) : 125-132. DOI: 10.16511/j.cnki.qhdxxb.2021.21.030

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Adaptive joint estimates of vehicle mass and road grades for small acceleration driving scenarios

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Abstract

Vehicle mass and the road grade are key input parameters for automated vehicle decision and control algorithms. These two parameters are difficult to know accurately, but are tightly coupled for small acceleration driving scenarios with small inertias. The accuracy and convergence speed of existing estimation algorithms need to be further improved. A vehicle dynamics model is used here with an adaptive unscented Kalman filter (AUKF) algorithm for rapid, accurate joint estimates of the vehicle mass and road grade during small acceleration scenarios. The prediction model has a large initial mass noise with an adaptive shrink coefficient designed to adjust the prediction error covariance matrices. The adaptive shrink coefficient is related to the estimation result and the prediction error covariance matrices. Simulations and vehicle tests for various initial conditions show that the AUKF algorithm can accurately estimate the vehicle mass and road grade with an estimated vehicle mass error of less than 3% and a root-mean-square error in the road grade of less than 0.4°. In addition, the AUKF algorithm gives better convergence speeds for the vehicle mass and road grade estimates with longitudinal accelerations less than 0.3 m/s² than the traditional UKF algorithm. When the initial mass error is within 40%, the estimated mass error converges to less than 3% within 10 seconds.

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vehicle mass / road grade / unscented Kalman filter / adaptive filter

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SUN Enxin, YIN Yuming, XIN Zhe, LI Shengbo, HE Jugang, KONG Zhouwei, LIU Xiupeng. Adaptive joint estimates of vehicle mass and road grades for small acceleration driving scenarios[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(1): 125-132 https://doi.org/10.16511/j.cnki.qhdxxb.2021.21.030

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