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- ÀúÀÚXudong Zhang, Dietmar Go hlich Àú
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- ÃâÆÇÀÏ2020-07-10
- µî·ÏÀÏ2020-12-21
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The effect of vehicle active safety systems is subject to the friction force arisingfrom the contact of tires and the road surface. Therefore, an adequate knowledge
of the tire-road friction coefficient is of great importance to achieve a good
performance of these control systems. This paper presents a tire-road friction
coefficient estimation method for an advanced vehicle configuration,
four-motorized-wheel electric vehicles, in which the longitudinal tire force is
easily obtained. A hierarchical structure is adopted for the proposed estimation
design. An upper estimator is developed based on unscented Kalman filter to
estimate vehicle state information, while a hybrid estimation method is applied as
the lower estimator to identify the tire-road friction coefficient using general
regression neural network (GRNN) and Bayes¡¯ theorem. GRNN aims at detecting
road friction coefficient under small excitations, which are the most common
situations in daily driving. GRNN is able to accurately create a mapping from
input parameters to the friction coefficient, avoiding storing an entire complex tire
model. As for large excitations, the estimation algorithm is based on Bayes¡¯
theorem and a simplified ¡°magic formula¡± tire model. The integrated estimation
method is established by the combination of the above-mentioned estimators.
Finally, the simulations based on a high-fidelity CarSim vehicle model are carried
out on different road surfaces and driving maneuvers to verify the effectiveness of
the proposed estimation method.
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A hierarchical estimator development for estimation of tire-road
friction coefficient
1. Introduction 41
2. Vehicle modeling 42
3. Hierarchical estimation algorithm design 46
4. Hybrid estimator design for tire-road friction coefficient 49
5. Simulation results 53
6. Conclusion 57
7. References 60