Tire-Road Friction Coefficient Estimators for 4WID Electric Vehicles on Diverse Road Conditions

doi:10.3901/JME.2019.22.080

Abstract

Abstract: Four-wheel independent drive (4WID) electric vehicle has large potential in dynamics stability coordination control, and the estimation of each tire-road friction coefficient is the foundation of its maneuverability control. The cost of visual sensors-based observation scheme can be high, and traditional Kalman filter has limited estimation accuracy and is not adapt well to nonlinear system with time-varying structures. Most model-based researches have studied the estimation methods for uniform road surfaces in depth, and not fully considered the observation schemes for joint and μ-split road surfaces. Strong tracking theory (STT) is introduced to unscented Kalman filter (UKF) for the construction of Strong Tracking Unscented Kalman Filter (STUKF) with higher filter accuracy and good adaptability to time-varying friction coefficient. After complex computation procedure of multiple fading factors has been considered, the dimension-reduction method of four dimensional observation model is discussed. Two lower dimension estimators with single fading factor matrix are built as so to observe four tire-road friction coefficients in real time. Compared with traditional four dimensional UKF-based estimator, improved parallel estimators based on STUKF algorithm could track true values of these coefficients more effectively on the cornering and straight driving conditions of the joint and μ-split roads.

Key words: four-wheel independent drive, tire-road friction coefficient, strong tracking theory, unscented Kalman filter, fading factor matrix

CLC Number:

U469

PING Xianyao, LI Liang, CHENG Shuo, WANG Hengyang. Tire-Road Friction Coefficient Estimators for 4WID Electric Vehicles on Diverse Road Conditions[J]. Journal of Mechanical Engineering, 2019, 55(22): 80-92.

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