An Enabling Tire-Road Friction Estimation Method for Four-in-Wheel-Motor-Drive Electric Vehicles

In this article, a particle filter (PF)-based tire-road friction estimation method is proposed for four-in-wheel-motor-drive electric vehicles (FIWMD EVs) by synthetically using the dual global positioning system (DGPS) and three low-cost inertia measurement units (IMUs). In the scheme, two independent PF-based road friction estimators are developed for straight driving and cornering conditions. For straight driving conditions, the longitudinal tire forces are first estimated using the output torque and rotational speed of motor, based on which a tire-road friction estimator is put forward by using a particle filter and the nonlinear relationship between longitudinal tire force and tire-road friction. For cornering conditions, the lateral tire forces and vehicle sideslip angle are estimated by using three IMUs and the DGPS. A PF-based tire-road friction estimator is established based on the nonlinear lateral tire characteristics. An estimation mode decision scheme is developed to determine which of the two PF-based estimators is used to update the tire-road friction estimate by considering both tire dynamics states and tire force characteristics. The accuracy and reliability of the proposed tire-road friction estimation scheme is verified under various maneuvers and road friction conditions through hardware-in-the-loop (HIL) tests. The results show that the proposed method exhibits high estimation accuracy, robustness, and computational efficiency.

Automated summary

This article proposes a particle filter-based tire-road friction estimation method for four-in-wheel-motor-drive electric vehicles, using dual global positioning system and low-cost inertia measurement units. The method includes independent estimators for straight driving and cornering conditions, and a decision scheme to update the friction estimate based on tire dynamics states and force characteristics. The proposed method shows high accuracy, robustness, and computational efficiency.