Estimation of Tire-road Friction Limit with Low Lateral Excitation Requirement Using Intelligent Tire

Tire-road friction condition is crucial to the safety of vehicle driving. The emergence of autonomous driving makes it more important to estimate the friction limit accurately and at the lowest possible excitation. In this paper, an early detection method of tire-road friction coefficient based on pneumatic trail under cornering conditions is proposed using an intelligent tire system. The previously developed intelligent tire system is based on a triaxial accelerometer mounted on the inner liner of the tire tread. The friction estimation scheme utilizes the highly sensitive nature of the pneumatic trail to the friction coefficient even in the linear region and its approximately linear relationship with the excitation level. An indicator referred as slip degree indicating the utilization of the road friction is proposed using the information of pneumatic trail, and it is used to decide whether the excitation is sufficient to adopt the friction coefficient estimate. The friction coefficient is estimated by the ratio of the normalized lateral force and the nonlinear adaptation of the slip degree. The tire forces and pneumatic trail are estimated by neural networks. The experimental validation demonstrates that the pneumatic trail has a good potential to precisely predict the friction coefficient at a low excitation under cornering conditions.