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Extended Kalman Filter Based Road Friction Coefficient Estimation and Experimental Verification
Technical Paper
2019-01-0176
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Accurate road friction coefficient is crucial for the proper functioning of active chassis control systems. However, road friction coefficient is difficult to be measured directly. Using the available onboard sensors, a model-based Extended Kalman filter (EKF) algorithm is proposed in this paper to estimate road friction coefficient. In the development of estimation algorithm, vehicle motion states such as sideslip angle, yaw rate and vehicle speed are first estimated. Then, road friction coefficient estimator is designed using nonlinear vehicle model together with the pre-estimated vehicle motion states. The proposed estimation algorithm is validated by both simulations and tests on a scaled model vehicle.
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Citation
Li, B., Sun, T., Fang, A., and Song, G., "Extended Kalman Filter Based Road Friction Coefficient Estimation and Experimental Verification," SAE Technical Paper 2019-01-0176, 2019, https://doi.org/10.4271/2019-01-0176.Data Sets - Support Documents
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| Unnamed Dataset 1 |
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